Find More, Miss Less

Accurately Identify
Emerging Environmental
Chemical Contaminants
Application Compendium

Identify Emerging Contaminants in Air, Water, and Soil
Today’s environmental analysis must be done more reliably, more efficiently, and withhigher quality results than ever before. Unfortunately, pharmaceuticals, PFAS, pesticides,
and other potentially toxic compounds can be difficult to detect in environmental andbiological matrices.
To complicate matters, several thousand chemicals present in the environment are
currently unknown and unidentified. Many of these chemicals could be toxic, but are notregulated in the environment, due to the absence of toxicological data.

Harness the power of accurate mass for
identifying emerging (or evolving) contaminants

Take the first step in identifying environmental
toxicity

Accurate mass high-resolution instruments from Agilent—along with simple, yet powerful software tools—provide acomplete workflow for identifying emerging and unknown
chemicals in the environment.

Along with regulated contaminants, unknown and unregulated
chemicals can put wildlife and humans at risk.

Agilent 6500 Series LC/Q-TOF and 7200 Series
GC/Q-TOF systems give you a complete chemical profile
of your samples. So you can characterize a wider rangeof contaminants. What’s more, these high-resolution
instruments can be operated at fast acquisition rates tocollect molecular ion and fragment information in MS/MS
mode. This maintains mass accuracy and isotope fidelity
for confident compound identification at environmentally
relevant concentrations.

Agilent SeaHorse analyzers and cell analysis measure cellularfunctions that are affected by exposure to environmental
toxins. This provides toxicity information in the environmental
samples. You can then identify chemicals that cause
toxicological effects using advanced mass spectrometry. This
combination of biological toxicity testing and identification iscalled “effects-directed” analysis.

What’s inside?
In this compendium, you’ll find high-resolution mass spectrometry applications for compounds such as pesticides,
PFASs, VOCs, SCCPs, and pharmaceuticals. These applications include the identification of new and previously unknown
environmental contaminants.
And as always, our product and application experts are available to help you maximize productivity. They can provide simple
workflows for identifying emerging chemicals while maintaining stringent standards for regulated compounds.

2

Table of Contents
Interactive file users: To access the appropriate information, click the titles.

To search for words or phrases
used in any of the listed application
notes, click the Search button.
SearchExposure in Environmental Matrices5

Screening and Identification with LC/Q-TOF
LC/Q-TOF Workflows for Comprehensive Micropollutant Analysis

6

Identification of Unknowns in Ground and Surface Water by LC/Q-TOF

16

Environmental Profiling of River Water Using
Q-TOF LC/MS and Mass Profiler Software

26

Screening for Emerging Chemical Contaminants in Water
Using LC/Q-TOF and Mass Profiler Professional Software

30

Screening and Identification of Emerging Contaminantsin Wastewater Treatment Plant Effluents

38

Accurate Mass Analysis of Hydraulic Fracturing Waters:
Identification of Polyethylene Glycol Surfactants by LC/Q-TOF-MS

54

Using a Chlorine Filter for Accurate-Mass Data Analysis
of Environmental Samples60

Identification of Unknown Microcystins in Alberta Lake Water

66

Identification and Fragmentation of Sucralose Using
Accurate-Mass Q-TOF LC/MS and Molecular Structure
Correlator Software

78

Sensitive Screening of Pharmaceuticals andPersonal Care Products (PPCPs) in Water

86

Screening and Identification with GC/Q-TOF

3

The Use of High Resolution Accurate Mass GC/Q-TOF
and Chemometrics in the Identification of Environmental
Pollutants in Wastewater Effluents

100

Analysis of Combustion Byproducts on Firefighter
Protection Equipment Using a Novel High-Resolution GC/Q-TOF

108

Table of Contents continued

4

GC/Q-TOF Workflows for Comprehensive Pesticide Analysis

116

Analysis of Extractable and Leachable (E&L) Compounds Using a
Low-Energy EI-Capable High-Resolution Accurate Mass GC/Q-TOF

128

Screening of Semivolatile Organic Compounds (SVOCs) on
Aerosol Particles Using the Agilent 7200 Series GC/Q-TOF System

134

Analysis of Biomarkers in Crude Oil142

A New Approach to the Analysis of Chlorinated Paraffins
by Gas Chromatography Quadrupole Time-of-Flight Mass Spectrometry

152

Environmental Exposure and Toxicity
Profiling in Biological Matrices

164

Analysis of Per- and Polyfluoroalkyl Substances (PFASs) in
Biological Fluid Using a Novel Lipid Removing Sorbent and LC-MS/MS

165

Using the Blood Exposome to Discover Causes of Disease

173

Analysis of Polycyclic Aromatic Hydrocarbons (PAH) andHydroxylated PAH Metabolites in Plasma and Urine Using
High-Resolution GC/Q-TOF

181

Exposure in Environmental Matrices

Nontargeted screening of suspect and unknown contaminants
Exposure to chemical contaminants in water, air, and soil present potential healthrisks for biota and humans. Concern about emerging and unknown environmental
contaminants has increased efforts to identify the scope and origin of trace chemicals.
Global screening initiatives have also been implemented for soil contamination,
wastewater treatment, and drinking water purification. Successful, effects-directedcharacterization of biologically and chemically relevant contaminants requires suspect
screening and nontargeted identification using advanced MS technology.
Agilent provides proven end-to-end solutions for emerging contaminant research.
Our LC and GC/Q-TOF systems let you characterize a sample’s complete chemical
profile. In addition, our simple, yet powerful software tools and databases makeidentifying nontargeted chemicals efficient. We also offer tools for suspect andnontargeted screening that deliver speed to results by reducing false positives—
without compromising data quality and spectral resolution.

Back to Table of Contents
Back to Introductionwww.agilent.com/chem/environmental

5

LC/Q-TOF Workflows forComprehensive Micropollutant
AnalysisTargeted Quantification, Suspect Screening, andUnknown Compound Identification

Application Note
Environmental

Authors

Abstract

Christoph Moschet andThis application note presents three complementary LC/Q-TOF workflows

Thomas M. Youngdesigned to provide comprehensive analysis of micropollutants in surface waters:

Department of Civil and• Targeted quantification

Environmental Engineering,
University of California,
Davis, CA, USA
Tarun Anumol
Agilent Technologies Inc.
2850 Centerville Rd,
Wilmington, DE, USA• Suspect screening with and without MS/MS spectra• Unknown compound identification
The first two workflows rely on the Agilent All Ions MS/MS accurate masscapabilities of the Agilent LC/Q-TOF system, Agilent MassHunter Qualitative
Analysis software, and Agilent Personal Compound Database and Libraries(PCDLs) to detect and confirm compound identities, with or without reference
standards. Agilent Molecular Structure Correlator (MSC) software is shown to aid
in the identification of unknown compounds, in this case transformation products(TPs).
The targeted quantification workflow was validated using 32 reference standards.
The workflow detected and quantified 25 compounds in at least one of the
51 surface water samples tested. The suspect screening workflow generated an
expanded list of 85 possible pollutants, of which 73 were subsequently positively
identified with an authentic standard (67 compounds) or by matching MS/MS
spectra (six compounds). The unknown compound identification workflow
identified five TPs that had not been identified using the targeted quantification orsuspect screening workflows.

Introduction

For compounds not identified through targeted analysis, thesuspect screening workflow, which uses Agilent All Ions
MS/MS and Personal Compound Database and Libraries(PCDLs), allows accurate mass determination and MS/MS
fragment confirmation of compounds. The screening workflow
allows the analyst to presumptively identify compounds
without analytical reference standards with a high degreeof confidence. The unknown compound identification
workflow attempts to identify compounds for which there
are not MS/MS fragment entries in the selected PCDLs, nor
are analytical standards readily available. In this workflow,
Agilent Molecular Structure Correlator (MSC) software
compares MS/MS fragments for compounds of interestto plausible candidate structures in additional databases
such as Chemspider, Pubchem, or custom databases. MSC
calculates scores based on the quality of the matches of theexperimental fragments with those predicted.

Comprehensive analysis of micropollutants in waste, surface,and drinking waters is necessary to confidently assess
exposure and risk. Traditional targeted screening workflows,
such as triple quadrupole MS methods, monitor and quantify
a predefined list of compounds using analytical reference
standards. However, a targeted approach will miss pollutants
not on the target list, and may underestimate exposure whenunexpected pollutants are present.
While triple quadrupole LC/MS systems are well suited fortargeted analysis, their methods require standards that are
not always easily obtained. In addition, they are not usefulwhen trying to identify new or unknown compounds in thesample. High-resolution, accurate-mass (HRAM) quadrupole
time-of-flight (Q-TOF) LC/MS analysis allows complementarysuspect screening and unknown compound identification
workflows, which, when used together with targeted
quantification, provide a more complete picture of thechemical profile of the sample analyzed (Figure 1).

This application note describes the use of targeted
quantification, suspect screening, and unknown compound
identification with LC/Q-TOF data acquisition and analysis
workflows to achieve broader, more confident characterizationof micropollutants, including transformation products (TPs),in surface water samples. The study LC- and GC-QTOF-MS
as Complementary Tools for a Comprehensive Micropollutant
Analysis in Aquatic Systems provides a detailed description of
the analytical results and their implications for environmental
monitoring [1]. Because not all of the compounds studied are
amenable to LC/MS analysis, the research also describes
a complementary GC/Q-TOF workflow that provides acomprehensive chemical profile of the sample.

Extract water samples with validated
extraction protocol

Perform All Ions LC/Q-TOF/MS Data
Acquisition, + and –ESI modes

Quantify target compounds using
Agilent MassHunter Quantitative
Analysis, [M+H]+ or [M−H]− quantifier
and two MS/MS qualifiers

25 Targeted
pesticides detected

Screen data for suspects using
Agilent MassHunter Find by Formulaand Pesticide and Water Contaminant
PCDLs

85 Compounds detected,
73 identified withstandards or MS/MS
spectra matches

Discover unknowns using predictedTransformation Product Custom
PCD/PDCL, and Agilent MassHunter
Find by Formula and MSC

5 TPs identified

Figure 1.

Experimental
Target compounds and standards
For the targeted quantification workflow, 32 LC/MS‑amenablepesticides were chosen for analysis of the surface water
samples (Table 1). The target compounds were chosen
to include compounds used in the area of the waters
sampled, and to represent pesticides of different classes andphysicochemical properties. Seventeen of the targets had
better sensitivity in positive ESI mode, while 15 had better
sensitivity in negative ESI mode. Eleven isotopically-labeledinternal standards were used for accurate quantification.
For method validation and quality control analyses, reference
standards for the target compounds were prespiked before
extraction and post-spiked before injection. Procedural blanks
were also used to look for blank contamination.

Complementary targeted quantification, suspect screening,
and unknown compound identification LC/Q-TOF workflows forcomprehensive micropollutant analysis.

2

Table 1.

Target Pesticides and Targeted Quantification Results

Target analyte

ESI
mode

Matrixfactor1

Method detection
limit (ng/L)2Absolute
recovery (%)3

Accuracy (%)4 Precision (%)5

2,4-D

–

2.6

2.6

95 %

160 %

18 %

2-Phenylphenol

–

1.2

1.2

75 %

170 %

6%

Azoxystrobin

+

1.7

0.2

95 %

128 %

1%

Boscalid

–

1.6

0.3

97 %

111 %

3%

Chlorantraniprole

+

2.7

2.7

95 %

87 %

3%

Clomazone

+

2.7

1.3

76 %

190 %

1%

Cyprodinil

+

3.1

0.3

91 %

118 %

3%

DEET

+

2.2

0.2

76 %

78 %

4%

Difenoconazole

+

1.7

0.9

95 %

104 %

1%

Dimethoate

+

3.8

0.9

92 %

62 %

5%

Diuron

–

1.7

0.2

92 %

102 %

1%

Fipronil

–

1.2

0.1

97 %

96 %

6%

Fipronil-desulfinyl

–

1.2

0.1

101 %

78 %

4%

Fipronil-sulfide

–

1.1

0.1

97 %

74 %

18 %

Fipronil-sulfone

–

1.2

0.1

96 %

113 %

1%

Hexazinon

+

3.0

0.3

91 %

117 %

3%

Imidacloprid

–

4.2

2.1

93 %

152 %

5%

MCPA

–

2.9

1.1

96 %

112 %

1%

Methomyl

+

2.7

13

93 %

104 %

2%

Methoxyfenozide

–

1.3

0.1

99 %

72 %

4%

Metolachlor

+

1.7

0.2

80 %

108 %

2%

Novaluron

–

1.2

0.6

74 %

91 %

2%

Pendimethalin

+

1.8

1.8

71 %

74 %

3%

Propanil

–

1.1

1.2

98 %

138 %

4%

Propoxur

+

2.6

1.3

76 %

83 %

1%

Pyriproxyfen

+

2.3

0.2

89 %

98 %

7%

Simazine

+

7.2

1.8

89 %

77 %

3%

Thiacloprid

+

4.1

1.0

93 %

97 %

6%

Thiamethoxame

+

2.0

1.0

92 %

108 %

3%

Thiobencarb

+

1.6

1.6

77 %

99 %

2%

Triclocarban

–

1.4

0.1

92 %

97 %

1%

Triclosan

–

1.2

1.3

89 %

89 %

2%

Equation 1.

Matrix factor =Equation 2.

Method detection limit (ng/L) =Equation 3.

Absolute recovery (%) =Equation 4.

Accuracy (%) =Concentration prespiked extract – Concentration unspiked extract
100 ng/mL

Equation 5.

Precision (%) =Standard deviation concentration prespiked extract (triplicate)
Average concentration prespiked extract (triplicate)Area STD 100 ng/mL
Area extract post spiked 100 ng/mL – Area extract unspiked
Instrument detection limit (ng/mL) × Matrix factor (–)
Concentration factor (mL/L)

Area prespiked extract – Area unspiked extract
Area post spiked extract – Area unspiked extract3

Sample preparation

The Q-TOF mass spectrometer was operated in both
positive and negative ESI modes to maximize compounddetection. Agilent MassHunter Workstation software wasused to acquire data (version B.07.00). The All Ions MS/MS
acquisition mode with collision energies (CEs) of 0, 10, 20,
and 40 V was used to obtain both precursor and fragment
ion data for all species. The All Ions MS/MS acquisitionmode simultaneously collects high- and low-CE scans. The
low CE scans allow the user to obtain precursor information,
while the higher CE scans provide fragment information thatenhances compound identification and confirmation whencomparing experimental spectra to those in the PCDL. Table 3
lists the Q-TOF mass spectrometer parameters.

Fifty-one 1-L surface water samples collected from the
Sacramento-San Joaquin River Delta in Northern California
were extracted using a mixed-mode solid phase extraction(SPE) cartridge. The cartridges were eluted sequentially with
6 mL of 50/50 methanol/ethyl acetate with 0.5 % ammonia,
3 mL of methanol/ethyl acetate with 1.7 % formic acid,and 2 mL of methanol, per Moschet; et al. [2]. The eluent
was evaporated to 0.2 mL and reconstituted to 1 mL with
nanopure water to obtain a 20 %/80 % methanol/water ratio
for injection into the LC/MS system.

LC/Q-TOF analysis
LC/MS analysis of the reference standards and sample
extracts was performed using an Agilent 1260 Infinity LC
coupled to an Agilent 6530 Accurate-Mass Quadrupole
Time‑of-Flight (Q-TOF) LC/MS system equipped with an
Agilent Jet Stream dual electrospray ionization (ESI) source.
The HPLC system included a binary pump, Agilent 1260
Autosampler, and an Agilent ZORBAX Eclipse Plus C-18,
2.1 × 100 mm, 1.8 µm column (959758-902). Table 2 lists the
LC parameters.

To facilitate identification of TPs not identified in the targeted
quantification or suspect screen workflows, the samples
with the highest abundances of plausible TPs were rerun intargeted MS/MS mode with the collision energy set to 20 V.

Table 2.

Table 3.

LC Parameters. (+): Positive ESI Mode; (-): Negative ESI Mode

Parameter

Value

Liquid chromatograph Agilent 1260 Infinity Binary LC
Analytical column

Agilent ZORBAX Eclipse Plus C-18,
2.1 × 100 mm, 1.8 µm

Q-TOF Mass Spectrometer Parameters

Parameter

Value

Mass spectrometer

Agilent 6530 Accurate Mass Q-TOF-LC/MS with
Agilent Jet Stream Technology

Ionization mode(s)Positive (+) and negative (–) ESI

Instrument mode

2 GHz extended dynamic range

Mass range

50–1,050 m/z

Gas temperature

300 °C

Drying gas flow

12 L/min

Nebulizer

25 psig

Sheath gas temperature

350 °C
11 L/minCapillary voltage

3,500 V (+), 3,000 V (–)

Fragmentor voltage

110 V

Injection volume

20 µL

Column temperature

30 °C

Mobile phase

A) Water + 0.1 % formic acid (+)/
Water + 1 mM ammonium fluoride (–)B) Acetonitrile + 0.1 % formic acid (+)/
Acetonitrile (–)

Sheath gas flow

Flow rate

0.35 mL/min

Scan speed

4.0 spectra/sec

Gradient

Time (min)
0.0
1.5
16.5
21.5

Collision energies

Agilent All Ions MS/MS: 0, 10, 20, 40 V
Targeted MS/MS:
20 V

Reference ions*(+) 121.0509 and 922.0098(–) 112.9855 and 1033.9881

Equilibration time

%A
98
98
0
0

* The positive mass reference ions were not used in some instances
because of interferences in some of the samples. The user can check forinterferences and use alternate reference ions if required.

3.0 minutes

4

Data processing and analysis

The suspect screening workflow used the Agilent Pesticide
and Water Screening PCDLs, in combination with the
MassHunter Qualitative Analysis software (version B.07.00)
Find by Formula algorithm. Find by Formula automaticallyextracts precursor ions from the All Ions MS/MS data using
the accurate-mass database in the PCDLs. When available
in the accurate mass library, the corresponding MS/MS
fragments are also extracted from the data. Precursor andcorresponding fragment ion peaks are plotted to score thequality of their correlation for each compound. The suspect
screening workflow data analysis parameters used areprovided in Figure 2.

Target compounds were quantified using Agilent MassHunter
Quantitative Analysis software (version B.07.00). The [M+H]+
or [M−H]− ion within the exact mass window of ±10 ppm
was used as the quantifier ion. The two most abundantunique MS/MS fragments for each compound were selectedfrom the MS/MS library spectra in the Agilent PCDLs
to use as qualifiers. The Agilent Pesticide PCDL for TOF
or Q-TOF LC/MS systems (1,684 compounds, 914 with
MS/MS spectra) and the Agilent Water Screening PCDL
(1,451 compounds, 1,157 with MS/MS spectra) were used.

Precursor
identification• Mass tolerance: 10 ppm• Allowed adducts: [M+H]+, [M+Na]+; [M-H]–, [M+F]–• Allowed charge state: 1

Counts60
Counts (%)

Find By
Formula• Database/Library Search• Agilent Pesticide PCDL
• Agilent Water Screening PCDL

Library spectrum
110
372.09787
100 PCDL spectra at CE: 20 eV
90
80
70
60
344.10297
50
301.08591
40
30
329.07950
216.06552
20
10 134.06004 172.03931
0
120
240
160
200
280
320
360
400
Mass-to-charge (m/z)
12.28950

Azoxystrobin
Mass: 403.1159
Score: 97.0

40
30
20
10
0

×10
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

12.0

12.2

5

Fragment ion
confirmation

Figure 2.

• Minimum number of fragments present: 1• Minimum coelution score: 85

12.6
12.8
13.0
Acquisition time (min)13.2

404.12303([C22H17N3O5]+H)+

CountsCounts (%)

Precursor ion
assessment• Mass accuracy (weight: 100)
• Isotope spacing (weight: 60)• Relative isotope abundance (weight: 60)• Minimum overall score: 70

12.4

405.12695([C22H17N3O5]+H)+

404.0

404.4

404.8
405.2
405.6
Mass-to-charge (m/z)

406.0

12.2

12.3

12.4
12.5
12.6
Acquisition time (min)

12.7

1.4
1.2
1.0
0.8
0.6
0.4
0.2
0

Suspect screening workflow: data analysis parameter settings and example. The precursor mass of the fungicide azoxystrobin(403.1168 m/z) is found at retention time 12.3, and its fragment ions are confirmed.

5

13.4

The unknown compound identification workflow can beused to identify compounds without a priori knowledge
using accurate mass and fragment data along with in silico
fragment prediction solfware. In this case, it relied on acustom PCD of potential TPs, which contained accuratemasses and molecular formulas. The Eawag Pathway
Prediction System (EAWAG‑PPS) [3] was used to generate
1,409 possible TP structures for the pesticides detected in the
study. After eliminating 71 structures due to the implausibilityof being ionized by ESI, the molecular formulas of theplausible structures were added to build the custom PCD. The
MassHunter Qualitative Analysis software Find by Formula
feature was used to screen the data for these plausible
molecular formulas. Because there were no MS/MS spectra
for these compounds in the custom PCD, the exact mass,isotope score, number of detections across the 51 samples,and retention time (RT) were used to produce a short list ofplausible matches.

Method detection limits for all target analytes ranged from
0.1 and 13 ng/L in water. All targets had absolute recoveries
between 70–110 %. The precision, calculated as the relative
standard deviation (%RSD) of triplicate injections on the
LC/Q-TOF system, was <10 % for 30 of the target analytes,
while the accuracy for 26 analytes was between 70 and
130 %.
Targeted screening detected 25 of the target compounds
among the 51 water samples tested (Figure 1).

Suspect screening
The All Ions MS/MS sample data collected in both positive
and negative ESI modes were screened for compounds
present in the Agilent Pesticide and Water Screening PCDLs
using the Find by Formula algorithm. Based on specified
adducts (Figure 2), the software automatically searched
the acquired data for the presence of the precursor ions ofcompounds stored in the PCDL, and assigned a cumulativescore to matches based on mass accuracy, isotopic spacing,and relative isotope abundance.

Water samples with the highest abundances of the short listof plausible candidates were rerun in targeted MS/MS mode
to obtain MS/MS spectra for processing by MSC software(version, B.07.00). MSC software was used to search thecustom PCD for compounds with the same exact mass as
the isolated mass. MSC software automatically compares
predicted in silico fragments of the structures in the custom
PCD (or in a web-based database such as ChemSpider or
PubChem) with the measured MS/MS spectra. All measured
MS/MS fragments that can be explained by each structure
were listed and scored based on a weighted match. It
is important to note that this workflow requires that thematching structures are present in the custom PCD. The
structures can be manually uploaded into the PCD using
mol‑files.

A score threshold of >70 was chosen (Figure 2) forcompounds to be considered for further evaluation.
Compound matches with higher scores were subsequently
evaluated for the presence of MS/MS fragment ionsmatching the compounds’ MS/MS spectra in the PCDL (whenMS/MS spectra were present in the PCDL). Compounds
without MS/MS spectra in the PCDL were tentatively
identified.
The validity of fragment ion matches was evaluated byscoring their coelution with their corresponding precursor
ions (coelution score). Using the spiked analytes, it wasdetermined that the presence of one fragment with acoelution score >85 was sufficient to identify compoundswith a low false positive rate, while producing a manageable
amount of data to process and review (Figure 2). Figure 2
also provides an example of the identification of the fungicide
azoxystrobin using the suspect screening workflow. Figure 3
provides a more detailed comparison of the theoretical (in the
PCDL) and measured isotope pattern, as well as the coelutionof the main fragments (from the PCDL) of the herbicide
fluridone.

To aid in the identification of unknown TPs, CFM-ID(http://cfmid.wishartlab.com/predict) was used to predict the
MS/MS spectra of the plausible TPs [4].

Results and Discussion
Targeted quantification
Table 1 shows the LC/Q-TOF positive and negative ESI
quantification results for the 32 target pesticide standards.
The pesticide standards were run on the LC/Q-TOF system
to obtain method validation parameters and to determine
method suitability for targeted quantification. The method
validation parameters, including method detection limits,
absolute recovery, accuracy, precision, and matrix factors, arepresented. The parameter details and their calculation havebeen described by C. Moschet; et al. [1].
6

0.9

×104
9
8
7

11.69

Counts1.0

330.1100
310.1038
309.0960
290.0976
294.0725

0.8
0.7Counts (%)

0.5

Isotope match score: 98

6
5
4
3
2
1
0

0.6

330.11003([C19H14F3NO]+H)+

331.11384([C19H14F3NO]+H)+

330.0

330.5
331.0
331.5
Mass-to-charge (m/z)

0.4

332.0

0.3
0.2
0.1
0

11.55

Figure 3.

11.6

11.65

11.7

11.75

11.8

11.85 11.9 11.95
Acquisition time (min)

12

12.05

12.1

12.15

12.2

12.25

12.3

Details of the theoretical and measured isotope pattern, along with molecular and fragment ions, identified for
fluridone using All Ions MS/MS and the PCDL.

Comparison of the abundance pattern of the 33 plausible
TPs with the concentration pattern of their potential parent
compounds in the water samples reduced the list of thosetentatively identified to 14.

After manual inspection, the suspect screening workflow
detected 85 compounds (53 in positive ESI, 26 in negative
ESI, and six in both positive and negative ESI). Of these, 67
could be confirmed unambiguously by a reference standard,
six could be confirmed tentatively with high confidence bymatching MS/MS spectra, and 12 compounds were rejected
because they were not confirmed by a reference standard
or due to implausible MS/MS fragments. The latter was thecase when there were no MS/MS spectra in the PCDL for thesuspected compound.

After rerunning the samples in targeted MS/MS mode,
evaluating the MS/MS spectra using the MSC software,comparing measured fragments to those predicted by CFM‑ID
and manually inspecting the results, seven compounds
were eliminated for having implausible MS/MS spectra(that is, having fragments that could not be explained by
the molecular structure). Thus, seven plausible TPs wereidentified using the workflow. Two of them were alreadydetected and confirmed using the targeted quantificationor suspect screening workflows. Therefore, five new TPs
could uniquely be identified by the unknown compound
identification workflow. Three of them were able to be
confirmed unambiguously by a reference standard, and twoof them remained tentatively identified because no reference
standard was commercially available.

Unknown compound identification
The MassHunter Qualitative Analysis software Find by
Formula feature was used to identify TPs in the water sample
data using plausible TP structures that had been added tothe custom PCD. This step yielded 110 matches. Inspectionof peak shape, signal-to-noise ratio (S/N), RT plausibility, andwhether the detected compound is theoretically ionizable inthe selected ionization mode left 33 plausible compounds.

7

Figure 4 shows an example of a transformation productof the herbicide dithiopyr (CAS 128294-56-4), which wastentatively identified with high confidence in several samples.
This compound, along with several MS/MS fragment ion
structures, was identified using MSC (score of 92.4). MSC
was able to identify and elucidate structures for 96.7 % of theions in the MS/MS spectra for this TP.
A

1. Predicted molecular structure ofcompound (MSC score: 92.4)

2. All the ions identified in the MS/MS
spectra with predicted structure scoresand potential candidates

3. Possible structures of selectedfragment ion with score andpotential fragment formula.

B
×10
8

3

4

F
F

7

CountsO

236.0134

OH

F

5

1

4

F F S

O CH

0

40

60

6

80

100

120

140

168.0059

210.0436

160

200

180

220

CH3

5.

F
F

N
C+

+ H3C
C

N

+
CH2

F

F
F F

+ H3C
C

N
+C
F

F F

C+

6.

C+

F

–

F

306.0573

C+ H
C
+

N

3. F

5

CH3

96.9584

C

F

F O
F

F

278.0611

68.9964

4.

–

F O

3

2

1

F

CH3

3 46.9968
2

S
F

2.

4
263.0368

N6

1. H3C

CH3

O
F

S
CH3

CH3

C O
+

F F

326.0637
370.1863
240

260 280 300 320
Mass-to-charge (m/z)

340

360

380

400

420

440

Figure 4A. Agilent MSC software window showing MS/MS fragments of a dithiopyr TP with CAS 128294-56-4 (MSC score 92.6). 1) predicted by MSC software;2) list of all ions identified with predicted structure scores and candidates; 3) possible structures and formulas of selected fragment ions. B) Mass
spectrum of a dithiopyr TP (§) predicted by MSC software; (*) predicted by CFM-ID.

8

Conclusions

References

LC/Q-TOF targeted quantification, suspect screening, andunknown compound identification workflows were applied
to the analysis of surface water samples. The targeted
quantification workflow was validated using 32 pesticide
standards. Twenty-five of them were detected among thesamples tested using the targeted quantification workflow.
The suspect screening workflow generated an expanded listof 85 possible pollutants, 73 of which were subsequently
positively identified. When applied to herbicide and pesticide
TPs, the unknown compound identification workflow
identified five plausible TPs not identified using the targeted
quantification or suspect screening workflows. Comparedto targeted analysis alone, the complementary workflows
enabled by the Agilent 6530 Accurate-Mass Q-TOF LC/MS
system provided more comprehensive, higher confidence
characterization of the micropollutants present, including
potential pesticide and herbicide TPs. Several morecompounds were identified that would have been missed if
only a targeted approach were used.

1. C. Moschet, et al. LC- and GC-QTOF-MS as Complementary
Tools for a Comprehensive Micropollutant Analysis in
Aquatic Systems. Environ. Sci. Technol. 51(3), 1553–1561(2017).
2. C. Moschet, et al. Alleviating the reference standard
dilemma using a systematic exact mass suspect screening
approach with liquid chromatography-high resolution mass
spectrometry. Anal. Chem. 85(21), 10312−20 (2013).
3. EAWAG-BBD Pathway Prediction System website; http://
eawagbbd.ethz.ch/predict/ (accessed 06/10/2016).
4. F. Allen, et al. Competitive fragmentation modeling of
ESI-MS/MS spectra for putative metabolite identification.
Metabolomics 11(1), 98−110 (2015).

The Agilent Pesticide and Water-Screening PCDLs, combinedwith the Agilent All Ions MS/MS accurate mass capabilitiesof the Q-TOF LC/MS system and Agilent MassHunter
Qualitative Analysis software, enabled presumptive
matching of acquired spectra with library spectra to confirm
compound identities, without the need to source standards.
Agilent Molecular Structure Correlator software aided
identification of TPs by correlating the unknown MS/MS
spectrum against multiple candidate structures in compound
databases.

9For More Information
These data represent typical results. For more information on
our products and services, visit our Web site atwww.agilent.com/chem.

www.agilent.com/chem
Agilent shall not be liable for errors contained herein or for incidental or consequential
damages in connection with the furnishing, performance, or use of this material.
Information, descriptions, and specifications in this publication are subject to change
without notice.
© Agilent Technologies, Inc., 2017
Printed in the USA
October 2, 20175991-8459EN

Application Note
Environmental

Identification of Unknowns in Groundand Surface Water by LC/Q-TOF

Authors
E. Michael Thurman,
Imma Ferrer
University of Colorado,Center for Environmental
Mass Spectrometry,
Boulder, CO
Jerry A. Zweigenbaum
Agilent Technologies, Inc.

Abstract
Accurate mass analysis by LC/Q-TOF with a series of accurate mass tools isused in this study to find target, suspect, and unknown pharmaceuticals and theirdegradation products in surface and groundwater along the South Platte River near
Denver, Colorado. The ability to detect unknown pharmaceuticals is used to examine
removal and transport of pharmaceuticals from a major metropolitan area (Denver)
to the South Platte River and eventually to alluvial groundwater nearby. This study
is a good example of using unknown identification to understand transport andremoval of pharmaceuticals in groundwater and surface water systems.

Introduction

Experimental

The importance of wastewater as asource of pharmaceuticals in surface
water has been studied extensively
since the late 1990s with a famous
review of the problem reported in 19981.
This review was followed by a study ofpharmaceuticals in surface water by the
U.S. Geological Survey2. Both studies
found that sewage wastewater was
the major source of pharmaceuticals inwater samples. Since then, thousandsof papers have been published on
the occurrence of pharmaceuticals insurface water and wastewater, including
many review articles3,4. However, theoccurrence of pharmaceuticals ingroundwater is much less studiedor reviewed4, despite the earliest
documented report of pharmaceuticals
in water being for groundwater impacted
by sewage3.

SamplesThis study describes the analytical
workflow and the set of analytical
tools with accurate mass that havesuccessfully been used to identify
pharmaceuticals and their degradants
using liquid chromatography andquadrupole time-of-flight mass
spectrometry (LC/Q-TOF-MS). In
addition, using these workflows, bothsuspect and unknown pharmaceuticals
found in the South Platte River near
Denver, Colorado were reported. Finally,
these pharmaceuticals and theirdegradants in alluvial groundwater
wells affected by the South Platte River
were measured using LC/Q-TOF-MS
analysis to better understand transportand removal of pharmaceuticals. This
Application Note is a compilation of
the many studies carried out using theAgilent 6500 Series of LC/Q-TOF-MS
for the discovery of pharmaceuticals insurface and groundwater.

2

Ten alluvial wells along the South PlatteRiver were sampled multiple times overa three-month period in 2016. Standard
ground and surface water sampling
methods were used, with both aperistaltic and bladder pump5. Samplingof the South Platte River and two alluvial
wells was also carried out in 2009–2010,
sampling monthly over one year. Allof the alluvial wells were in hydrologic
contact with the South Platte River. The
South Platte River itself was sampled bygrab sample. Figure 1 shows the locationof the alluvial wells along the river.

Analytical methods
The separation of the analytes
was carried out using an Agilent
1290 Infinity II LC equipped with areversed‑phase C8 analytical column of
150 mm × 4.6 mm and 3.5 µm particle
size (Agilent ZORBAX Eclipse XDB-C8).
Column temperature was maintained
at 25 °C. The injected sample volume
was 10 µL. Mobile phases A and B
were water with 0.1 % formic acid, andacetonitrile, respectively. The optimized
chromatographic method held the initialmobile phase composition (10 %A)
constant for five minutes, followed
by a linear gradient to 100 %A after
30 minutes.
The HPLC system was connected to an
Agilent 6545 LC/Q-TOF equipped withan Agilent Jet Stream source. The data
recorded were processed using Agilent
MassHunter software.

Figure 1. Location of groundwater wells along the South Platte Riverapproximately 30 miles north of Denver, Colorado5.

Sample preparation

Table 1. The tools used for unknown and suspect analysis.

Samples were concentrated using an
offline solid phase extraction (SPE)technique that allowed the sample
to be analyzed multiple times for MS
analysis, MS/MS analysis, and to reachthe required ng/L concentration level. A
100 mL water sample was processedusing SPE on a Gilson robotic system.
The sample was eluted with 5 mL of
methanol, and was then evaporated
under nitrogen to 0.5 mL for a final
concentration factor of 200 fold. Thismethod is a generic method thathas been used successfully for
antidepressants in surface waters andwastewaters6.

Hardware tools•

Biochemical pathways18,19

Results and discussion•

Biodegradation pathways6,19•

UV degradation and chemical oxidation20,21

Analytical workflow and tools used•

Correlation pathways

The analysis of groundwater and surface
water samples required the adoptionof a workflow method that allows forboth suspect and unknown detectionof pharmaceuticals. Table 1 shows
the set of tools used for suspect andunknown analysis, while Figure 2 shows
the workflow system that allows for athorough analysis of unknown samples
using an iterative system. The four typesof tools include:•

Hardware•

Software•Ion chemistry•

Physical, chemical, and biologicaltools related to surface andgroundwater chemistryThis approach is highly effective at
the process of suspect and unknown
identification in surface and groundwater
samples for pharmaceuticals.

•High resolution and accurate mass7–8, 25•

All Ions, Auto MS/MS, Pseudo MS3 (22–24)

Software tools•

Databases, libraries14•

Kendrick mass, mass structure correlator, elemental forcing routines,
isotope calculator14–16, 26

Ion chemistry tools•

Adduct formation (rule of 5), fragmentation tools (diagnostic andfragment ions), rules of fragmentation (N-rule and odd and evenelectron ion rules)8,9,16,17

Physical and biochemical tools—thinking outside the box

Tools used
The workflow in Figure 2 begins witha suspect list being searched in theaccurate mass file acquired in TOF
mode. The suspect list differs from atargeted list in that only the exact mass
is known for a suspect (sometimes aretention time may be known but nostandard is currently available, which isthe de facto definition of a suspect ornontarget list). A targeted compound
will have retention time and several
confirmation ions using accuratemass as a confirmation based on aconcept of identification points7. The
suspect/nontarget list used was a listof 100 common pharmaceuticals thathave been found in surface water andwastewater8.

Database search for unknown orsuspect list

Unknown or suspect identification
followed by Auto MS/MS

Lift created by correlation
for new compounds

Compounds identified followed bymetabolite search with diagnostic ions

Putative pharmaceutical metabolite
MS/MS structural correlator identification
Figure 2. Analytical workflow chart for unknown and suspect identification.

3

Two approaches are possible, either an
Excel database (CSV file) or an Agilent
database search, called a Personal
Compound Database (PCD.cdb) file. A
personal compound database and library(PCDL.cdb) file will also contain MS/MS
spectra, in the library portion. The
PCD.cdb file may also include a structure
as a mol file that can readily be imported
into the Molecular Structure Correlator
software for fragment verification. Bothare easy to make and use.
The 22 compounds detected andconfirmed in this study include:•

Beta blocker pharmaceuticals forblood pressure control•

Antibiotics•

Antidepressants•Caffeine•

Sucralose, a low-calorie sweetener

The beta blockers, antibiotics, andantidepressants were:•

Atenolol•Azithromycin•

Bupropion•

Clarithromycin•

Codeine•Dextrorphan•

Diltiazem•

Diphenhydramine•

Erythromycin anhydrate•

Gabapentin•

Lamotrigine•

Metoprolol•

Oxycodone•

Sulfamethoxazole•

Thiabendazole•

Trimethoprim•

Venlafaxine•

Various metabolites of thesepharmaceuticals

4

In addition to using the tools in Table 1
and the workflow in Figure 2, metabolites
were found using different tools, suchas diagnostic ions (Box 3, Figure 2). The
concept of diagnostic ions means thatpharmaceuticals of a specific chemicalfamily will often have similar fragment
ions or ions that are diagnostic of theirstructure. The publications by Ferrer
and Thurman8,9 list approximately threefragment ions that may be used as
diagnostic ions for the identification ofrelated pharmaceuticals and pesticides
either in the same family or metabolites
of detected compounds.
A good example of this tool is thediscovery of lamotrigine glucuronide,
which was found in the South PlatteRiver using several tools, including botha chlorine filter and diagnostic fragment
ion10. The diagnostic ion in this case was
the m/z 256 ion, which is the same massas the protonated molecule of the parent
compound, lamotrigine. This compound
was eventually identified after MS/MS
and standard matching10.
The Molecular Structure Correlator
shown in Box 5 in Figure 2, is anotheruseful tool. Before a standard ispurchased, it is possible to carry out
MS/MS. The accurate mass fragments
can then be considered using thestructure correlator, which, if possible,
assigns the correct accurate mass toeach of the fragments. This tool is afavorite for suspect analysis, since itis rare that two compounds have thesame fragment ions, though it does
occasionally happen11, as was the casewith an identification of tramadol and ametabolite of venlafaxine.

Finally, Box 6 in Figure 2 shows thepower of iterative use of the workflow
chart. There can be correlation, or lift,
between compounds. Lift is a marketing
term indicating sales increase with a
specific advertisement. Here, we use lift
to mean that a compound is present due
to the sale of this compound as either a
mixture or given with another compound.
These compounds do not correlate inwater samples by concentration due
to different chemistries and removal
processes during water treatment andtransport. Thus, the presence of one
may add a lift or give an opportunity forthe other compound to be present. Thisis the case where two compounds are
related by use but do not necessarily
correlate in a 1:1 sense. A good example
is the detection of sulfamethoxazole in
the South Platte River (Table 2), whichis commonly taken as an antibiotic forurinary infections, and is combined withtrimethoprim. When the accurate mass
for trimethoprim is searched, it is foundat a low intensity, below the cutoff ofthe suspect search by database, but thecompound is present at trace levels. It
is preferentially removed by wastewater
treatment but may still be present at
just above the limit of detection of theanalysis method, which shows thepower of using the workflow chart, foreach compound detected in the suspect
analysis or unknown identification.

Pharmaceuticals in alluvial
groundwater
Eight compounds were frequently foundin the alluvial groundwater 100–500 m
from the South Platte River. These
compounds were:Table 2. LC and MS and MS/MS instrument conditions used in this study.
LC conditions for the 1290 Infinity II LC
ColumnZORBAX Eclipse XDB-C8 150 mm × 4.6 mm and 3.5 µm (p/n 963967-906)

Column temperature

25 °C

Injection volume

10 µL

•

Bupropion

Mobile phaseA) AcetonitrileB) 0.1 % formic acid in water•

Caffeine

Run time

30 minutes•

Carbamazepine

Flow rate

0.6 mL/min•

Gabapentin, a metabolite ofcarbamazepine

Gradient

90 %B at time 0, hold for 5.0 minutes,
gradient to 100 %B at 30 minutes,
then 10 minutes post run time•

Lamotrigine•

Sucralose•

Sulfamethoxazole

This was a decrease from the 22compounds confirmed in the South
Platte River. The 14 compounds thatwere removed by bank filtration or diluted
below detection levels as the river water
flowed to the alluvial wells included:

MS conditions positive ion mode
Sheath gas temperature

350 °C

Sheath gas flow

11 L/min

Gas temperature

250 °C

Desolvation gas flow-rate

10 L/min

Nebulizer pressure

45 psiCapillary voltage

3,500 V

Nozzle voltage

0V

Skimmer voltage

65 V

Octopole RF

750 V

Accurate mass spectral range

50–1,000 m/z
190 V

•

Atenolol•Azithromycin

Fragmentor voltage•

Clarithromycin

Auto MS/MS conditions•

CodeineQuadrupole isolation width

1.3 m/z•Dextrorphan

Collision energies

15, 30 eV•

Diltiazem

MS mass range

100–1,000 m/z

MS acquisition rate

4 spectra/second•

Diphenhydramine

MS/MS mass range

40–700 m/z•

Erythromycin anhydrate

MS/MS acquisition rate

4 spectra/second•

Metoprolol

Threshold

25,000 counts•

Oxycodone

Relative threshold

0.01 %•

Thiabendazole

Active exclusion

Enabled, excluded after 1 second, released after 0.2 minutes•

Trimethoprim

Static exclusion

118–123 m/z
700–1,000 m/z•

Venlafaxine and its metabolite

Model

Common organic molecules, only singly charged precursors, sort precursors
by abundance, scan speed varied on precursor abundance, with a target of
50,000 counts/spectrum

Purity100 % with a purity cutoff of 30 %In a 2009–2010 sampling of agroundwater well much closer to theSouth Platte River, approximately 50 m,
atenolol, diphenhydramine, venlafaxine,
and its metabolite were also detected,
which shows minimal transport for thesecompounds.

5

Sucralose
Gabapentin
35

HO

100 %

O
30

Concentration red sucralose (ng/L)

Sucralose was 50 % of the concentration
found in the South Platte River,while gabapentin reached the same
concentration as in the river. These twocompounds can then be compared tothe other pharmaceuticals found by
LC/Q-TOF-MS analysis. For example,
Figure 4 shows the removal of atenolol
and diphenhydramine relative to theSouth Platte River. There is 90 % removal
of atenolol, a blood pressure regulator,
and 99 % removal of diphenhydramine,an over-the-counter antihistamine
medication. Both compounds arethought to be adsorbed to the sedimentsof the alluvial aquifer.

×103
40

NH2

25
Gabapentin
20
Note three-month delay
in maximum concentrations
15
HO

10

50 %

5

OH

Cl

HO
O

HO

O
OH

0

Cl
O

Cl

Sucralose

715
8- -0921
9- -091
10 6-0-1 9
11 6-0-1 9
12 7-0-2 9
1
1- -0927-1
3- 0
33- 1030
4- -1016
4- -10
29
-1
6- 0
310

Figure 3 shows a good example
of conservative transport (that is,nonremoval), which includes both apharmaceutical and a sweetener. They
are gabapentin and sucralose. The
concentration of these two compounds
in the South Platte River were either
identical or decreased only by 50 % whenmoving through alluvial groundwater,even at distances of up to 500 m fromthe river.

Figure 3. Conservative tracers of alluvial groundwater flow with apharmaceutical, gabapentin, and a sweetener, sucralose.

Mixed mode sorption examples: cation exchange and hydrophobic effect
1,600
1,400

Atenolol
90 % Removal

600

Groundwater
South Platte River
OH
O

1,200
H2N

O

Diphenhydramine
99 % Removal

500
H
N

CH3
CH3

400
O

1,000
300
800
200
600
400

100

0

0

715
8- -0921
9- -091
10 6-0-1 9
11 6-0-1 9
12 7-0-2 9
1
1- -0927-1
3- 0
3
3- -1030
4- -1016
4- -10
29
-1
6- 0
310

50

715
8- -0921
9- -091
10 6-0-1 9
11 6-0-1 9
12 7-0-2 9
1
1- -0927-1
3- 0
33- 1030
4- -1016
4- -10
29
-1
6- 0
310

200

Figure 4. Removal of atenolol and diphenhydramine in alluvial groundwater relative to the South Platte River.

6

Groundwater
South Platte RiverN

Although it is not clear why there isremoval, it appears both sorption
caused by the hydrophobic effect andby cation exchange could be importantmechanisms in the removal of thesepharmaceuticals as they transport
through groundwater. Figure 5 shows
another example of this removal
process of pharmaceuticals from alluvial
groundwater for venlafaxine and its
metabolite, desmethylvenlafaxine.

In this example, venlafaxine and its major
metabolite are 60 % removed duringtransport to alluvial wells close to theSouth Platte River. The amine grouppresent may be partially protonated at
the pH of groundwater (~7.5). Thus,
cation exchange and the hydrophobic
effect are in play to remove thisantidepressant and its metabolite fromgroundwater. These two compounds arealso important in that they are sources
for N-nitrosodimethylamine (NDMA)
during water treatment when using
chloramines for water purification12.
Apparently, the dimethylamine groupreacts with chloramine to form NDMA12.

Mixed mode sorption partially effective
1,400

1,400
South Platte River

Desmethylvenlafaxine
60 % Removal

Venlafaxine
Metabolite

1,200

Well 10A
South Platte River

1,200
OH

N

1,000

800
O

1,000

800

400

400

200

200

0

0

7-

715
8- -0921
9- -091
10 6-0-1 9
11 6-0-1 9
12 7-0-2 9
1
1- -0927-1
3- 0
33- 1030
4- -1016
4- -10
29
-1
6- 0
310

600

15
8- -0921
9- -091
10 6-0-1 9
11 6-0-1 9
12 7-0-2 9
1
1- -0927
3- 10
3
3- -1030
4- -1016
4- -1029
6- 10
37- 10810

600

Figure 5. Venlafaxine and desmethylvenlafaxine in alluvial groundwater within 50 m of the South Platte River.

7

Finally, one of the important uses ofalluvial groundwater is as a drinking
water source, especially along rivers
in the arid western United States. In
fact, the South Platte River is a good
example since it flows from Denver tonear Kansas City, MO before joining
the Missouri River. Along this 600 mile
stretch, alluvial groundwater is a drinking
water source for many communities in
Nebraska and eastern Colorado. Thus,
the results reported here have important
impact for drinking water standards, andshows how important the analysis ofsuspect and unknown compounds are
for water quality in the arid west wherewater re-use is important. Water re-use inthis sense means that wastewater may
be purified by infiltration to groundwaterand later consumed as a drinking-water
source.

8600

Lamotrigine
Carbamazepine
Sulfamethoxazole

Well 9500

Concentration (ng/L)

Figure 6 shows three pharmaceuticals
that were nearly always found in alluvial
groundwater 100–500 m from theSouth Platte River. The compounds
include carbamazepine, lamotrigine,and sulfamethoxazole. They are two
antidepressants and one antibiotic,
respectively. The compounds may vary
radically in concentration over time, withlamotrigine always being of the highest
concentration, a trend noted in theliterature for wastewater10. These threecompounds are definitely importantfor future studies of groundwater andpharmaceuticals. It is also importantto realize that concentration levels mayvary quickly because of the river water
source, which is affected by wastewater
output from the Denver area, in thiscase, and by the seasonal uses of many
pharmaceuticals.

400

300

200

100

0

1

2

3

4
5
6
7
8
9
10
Sampling times over two months

11

12

Figure 6. The concentration of three pharmaceuticals in groundwater over a two-month period.

Conclusions
The identification of unknowns requires
a complex set of tools, which aredescribed in this Application Note andare shown in Table 1. There are four
major categories of tools: hardware,
software, ion-chemistry, and physicaland biochemical tools. The reference
set included in this Application Noteand shown in Figure 2 are a guide forexamples of how to apply these various
tools. Both hardware and softwaretools continue to evolve with improved
resolving power and mass accuracy. The

use of auto MS/MS is useful since theevolution of accurate mass databases.
The application of ion chemistry tools
and physical and biochemical tools is anarea of research on accurate mass thatdevelops around the goal of the user.
The type of problem to be solved, forexample, a pharmaceutical class such as
opiates, will dictate the ion chemistry andbiochemical pathways. These tools areenhanced by software, but are driven by
the user’s application. Finally, the studyof unknowns will continue to develop
as more applications and problems are
addressed by LC/Q‑TOF analysis.

References

9.

Ferrer, I.; Thurman, E. M.
Multiresidue method for the
analysis of 101 pesticides andtheir degradates in food and water
samples by liquid chromatography
time of flight mass spectrometry, J.
Chromatog. A 2007, 1175, 24–37.

1.

Halling-Sorenson, B., et al. 1998,
Occurrence, fate, and effectsof pharmaceuticals in theenvironment-A review: Chemosphere,
36, 357–394.

2.

Kolpin, D.W. et al. Pharmaceuticals,
hormones, and other wastewatercontaminants in U.S. streams,
1999–2000: A national
reconnaissance, Environmental
Science and Technology 2002, 36,
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10. Ferrer, I.; Thurman, E. M.
Identification of a new
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metabolite liquid chromatography
time of flight mass spectrometry:
Analytical Chemistry 2010, 82,
8161–8168.

Heberer, T. Occurrence, fate, andremoval of pharmaceutical residues
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11. Ferrer, I.; Thurman, E. M. High
resolution mass spectrometry(LC/Q-TOF/MS) for the detection ofpharmaceuticals in water, Agilent
Technologies Application Note,publication number 5991-3261EN,
2013.

3.

4.

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Lapworth, D. J. et al. Emerging
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Pollution 2012, 163, 287–303.
Mauch, K., et al. Analyticalcomparison of HydraSleeve
to low‑flow sampling in NPS
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Groundwater Monitoring Review: Inreview 2018.

12. Hanigan, D.; et al.
LC/Q-TOF-MS fragmentationof N-Nitrosodimethylamine
precursors in drinking watersupplies is predictable and aids intheir identification, J. Hazardous
Materials 2017, 323, 18–25.

6.

Writer, J. H.; Ferrer, I.; Thurman, E. M.
Widespread occurrence ofpharmaceuticals and metabolites
in 24 Minnesota rivers andwastewaters, Science of the Total
Environment 2013, 461, 519–527.

13. Ferrer, I.; Thurman, E. M.;
Zweigenbaum, J. A. Analysisof environmental samples withultra‑high resolution LC/Q-TOF
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Application Note, publication number
5990-6430EN, 2010.

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Thurman, E. M.; Ferrer, I.;
Zweigenbaum, J. A. High resolution
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Chemistry 2006, 78, 6702–6708.

14. Agilent Technologies, Inc.
MassHunter Forensics andToxicology PCF and PCDL,
G6855‑90012, Agilent Technologies
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Ferrer, I.; Thurman, E. M. Analysisof 100 pharmaceuticals and theirdegradates in water samples byliquid chromatography time-of-flightmass spectrometry, J. Chromatog. A
2012, 1259, 148–157.

15. Agilent MassHunter Molecular
Structure Correlator Software,
G3335-90126. Agilent Technologies,
Inc. Website, 2011.

8.

16. Thurman, E. M.; Ferrer, I.; et al.
Analysis of hydraulic flowback andproduced waters using accuratemass: Identification of ethoxylated
surfactants, Analytical Chemistry
2014, 86, 9653–9661.
17. Thurman, E. M.; Ferrer, I.; et al. The
even-electron rule in the electrospraymass spectra of pesticides, Rapid
Commun. Mass Spectrom. 2007, 21,3855–3868.
18. Thurman, E. M.; et al. Identificationof imidacloprid metabolites in onion(Allium cepa L.) using high resolution
mass spectrometry and accuratemass tools, Rapid Commun. Mass
Spectrom. 2013, 27, 1891–1903.
19. Thurman, E. M.; Ferrer, I. Liquid
chromatography time-of-flightmass spectrometry with metabolic
profiling of human urine as a toolfor environmental analysis ofdextromethorphan, J. Chromatog. A
2012, 1259, 158–166.
20. Keen, O. S.; Ferrer, I.; Thurman, E. M.
Degradation pathways of lamotrigine
under advanced treatment by direct
UV photolysis, hydroxyl radical, andozone, Chemosphere 2014, 117,
316–323.
21. Fernandez-Ramos, C.; Ferrer, I.;et al. Identification of prometon,
deisopropylprometon,
and hdyroxyprometon in
groundwater by high resolution
liquid chromatography/mass
spectrometry, Science of the Total
Environment 2014, 497, 459–466.
22. Padilla-Sanchez, J. A.;
Thurman, E. M.; et al. Identificationof pesticide transformation products
in agricultural soils using liquid
chromatography/ time of flight mass
spectrometry: Rapid Commun. Mass
Spectrom. 2012, 26, 1091–1099.

923. All Ions MS-MS: Targeted screening
and quantitation using Agilent
TOF and Q-TOF systems, Agilent
Technologies, p.10, publication
number 5991-2465EN, 2013.
24. Ferrer, I.; Thurman, E. M.;
Zweigenbaum, J. A. Use of Auto
MS/MS Methods using MassHunterfor the identification of small
molecules, Agilent Technologies
Application Note, In Press 2018.
25. Ferrer, I.; et al. Both high-resolution
chromatography and accurate mass
spectrometry are essential for the
analysis of isobaric pesticides incomplex food matrices, Agilent
Technologies Application Note,publication number 5991‑5216EN,
2014.
26. Thurman, E. M.; Ferrer, I. Accuratemass analysis of hydraulicfracturing waters: Identification ofpolyethylene glycol surfactants by
LC/Q-TOF-MS, Agilent Technologies
Application Note, publication number
5991‑5473EN, 2015.

www.agilent.com/chem
This information is subject to change without notice.
© Agilent Technologies, Inc. 2018
Printed in the USA, May 29, 20185991-9469EN

Environmental Proﬁling of River
Water Using Q-TOF LC/MS and
Mass Proﬁler Software

Application Note
Environmental

Authors

Abstract

Dr. Christian Zwiener

The demand for the detection and identiﬁcation of emerging contaminants in the

University of Tübingenenvironment requires a new approach to environmental screening that employs

Tübingen, Germany

accurate mass LC/MS analysis and rigorous data analysis with a statistical

Tiffany Payne
Agilent Technologies, Inc.
Santa Clara, California

software package. This method uses an Agilent 6550 iFunnel Q-TOF LC/MS
with Agilent MassHunter Proﬁler software to investigate river water samples
taken upstream and downstream from a water treatment plant. The presenceof contaminants in the downstream versus the upstream sample indicates that
contamination is taking place. These contaminants were extracted from thedownstream water sample data and submitted to an accurate mass databaseand library for identiﬁcation. Ultimately, 890 compounds were detected in thedownstream sample, and 21 were successfully identiﬁed.

Introduction
Emerging contaminants in the environment have been of increasing concern toenvironmental scientists and the public in many parts of the world. Pesticides,
herbicides, pharmaceuticals, corrosion inhibitors, and ﬂame retardants are contaminants
that pose a threat to the quality of surface, groundwater, and drinking water. They also
adversely affect aquatic ecosystems and public health1,2. However, less than 0.1 % ofoxidative stress on cells from water samples could be linked to the effects of known
contaminants3. This clearly demonstrates the demand for further screening approaches.
The detection and quantitation of known contaminants in the environment usingmass spectrometry is well established and understood, but these techniques fail todetect unexpected contaminants. As a result, new tools and techniques have beendeveloped for a truly untargeted approach using high resolution mass spectrometry andchemometric (statistical) analysis.

The full approach uses an Agilent
6550 iFunnel Q-TOF LC/MS system,
Agilent Mass Proﬁler software, andAgilent MassHunter Qualitative analysis
software to analyze, extract, and identify
unanticipated contaminants in river water
samples.

Experimental
Water samples were collected from theAmmer River, Germany, in locations
upstream and downstream from awastewater treatment plant. Enrichment
was performed using solid phaseextraction (HLB and ENBV+) at a
preconcentration factor of 500.
Separation was carried out using an
Agilent 1260 Inﬁnity HPLC system
consisting of an Agilent 1260 Inﬁnity
Binary Pump (G4220A), an Agilent 1260
Inﬁnity High Performance Autosampler(G4226A), and an Agilent 1260 Inﬁnity
Thermostatted Column compartment(G1316C).

In the water sample taken downstream
from the water treatment plant andextracted in triplicate, 1,564 features weredetected. Most of these were unique tothe downstream sample (1,043 features),revealing the considerable impact ofwastewater treatment plant efﬂuentson surface water quality. Data reduction
included alignment of retention timeand mass data, blank subtraction, andthe occurrence in all three replicates.
The remaining 1,043 features attributed
to wastewater efﬂuents were further
examined for potential identiﬁcation.
The accurate mass for the features ofinterest were used by Molecular Formula

1,000
900
800
700
600
500
400
300
200
100
0

The preliminary identiﬁcation of thesigniﬁcant features in the downstream
water samples by searching the sum
formulae in databases yielded 18 hitswith a homemade database of more than
450 contaminants relevant for the aquatic
environment, 144 hits with an Agilent
Forensic Toxicology Personal Compound
Database, and 26 with an Agilent
Pesticide Personal Compound Database.
Relevant compounds were selected for

A

UHPLC column

Phenomenex Synergi
Polar-RP, 150 × 3 mm, 4 µm
at 35 °C

Mobile phaseA) water +0.1% formic acidB) methanol +0.1% formic acid
Min

%B

0

5

1

5

10

95

17

95

Stop time

17 minFlow rate

0.3 mL/min

1,000
900
800
700
600
500
400
300
200
100
0

0

2

4

B

6

10
8
Retention time (min)

12

14

16

18

14

16

18

1,043 features present only in downstream WWTP

Mass (Da)Experimental parameters

Gradient program

The data were analyzed in MassHunter
Qualitative Analysis rev. B.06.00 using
the Molecular Feature Extractor (MFE)
algorithm. MFE links MS data derived
from a particular molecule of interest, andcompiles the result into a chromatogram.
Once the molecules (features) in aparticular sample are extracted, the data
are submitted to the Mass Proﬁler (MP)for statistical analysis.

Generator (MFG) software to calculate
and propose molecular formulae andwere assessed for accuracy using ascore derived from the isotopic pattern,
isotopic spacing, and the difference
between the theoretical exact mass of
the assigned formula and the acquired
accurate mass for the feature. For
example, m/z 264.1967 was given ascore of 97.1 for C16H25NO2 based on
combined scores of 94.5 for the massdifference, 99.7 for the isotopic pattern,
and 99.4 for the isotopic spacing. A totalof 894 sum formulae could be assigned
to the measured accurate masses using
the elements C, H, N, O, S, P, and Cl at amass accuracy of 5 ppm.

Mass (Da)An Agilent 6550 iFunnel Q-TOF wasoperated with MassHunter Acquisition
rev. B.05.01 in 2 GHz extended dynamic
range mode with an acquisition rate of
1 scan/sec in MS mode and 3 scans/sec
in MS/MS mode with mass-dependent
collision energy setting for a targeted
MS/MS inclusion list.

Results and Discussion

0

2

4

6

8
10
Retention time (min)12

Figure 1. Features found by MFE in river water samples downstream of a WWTP. A) all features,B) features unique to the input of a WWTP.

2

For compounds not found in the accuratemass databases or libraries, a search of
the sum formulae in general chemical
databases such as ChemSpider and
PubChem was performed. These searches
resulted in several hundreds of hits forpossible structures. To narrow down
and rank the positive hits for one sum

100
80
60
40
20
0

matching calculated with measured massfragments. The most probable chemical
structures were selected from the highranked hits of MSC, as the example ofiopamidol reveals (Figure 3). The ultimate
unequivocal identiﬁcation should bevalidated by an authentic standard.

194.09570
100.00A

H2N

Acquired spectrum

Abundance

The resulting accurate MS/MS data werethen submitted to an Agilent Forensic
Toxicology Personal Compound Databaseand Library for identiﬁcation. Figure 2
shows an example of an identiﬁcationof carbamazepine, an anticonvulsive
and mood-stabilizing drug, by perfectmatching of the acquired and librarymass spectra. This result reveals
the importance of the availability ofcomprehensive mass spectral libraries
with accurate mass fragmentation
information4.

formula, in silico fragmentation was
performed using Agilent Molecular
Structure Correlator (MSC) software. The
input data are the molecular formula andthe measured accurate mass MS/MS
data for the unknown compound. The
MSC software then submitted thesum formula to a ChemSpider searchand ranked the positive hits based on

117.0118 145.00890
0.25
1.59

O
N

179.07179
1.37

237.10159
11.73

100 110 120 13 140 150 160 170 180 190 200 210 220 230 240 250 160
m/z
194.09642
100.00

B
100
80
60
40
20
0

Abundance

further investigation and identiﬁcation.
These compounds were compiled into
a compound list, where precursor ionswere automatically transferred to a list for
MS/MS analysis (MS/MS inclusion list)in a further Q-TOF LC/MS measurement.

Library spectrum
179.07295
1.19

237.10223
7.02

100 110 120 13 140 150 160 170 180 190 200 210 220 230 240 250 160
m/z

Figure 2. Structure, acquired spectrum (A) and library spectrum (B) for carbamazepine, a drug detected in
the downstream water sample.

Figure 3. Identiﬁcation of iopamidol using accurate mass data for precursor and products ions, Agilent MassHunter Molecular Structure Correlator (MSC)
software, and ChemSpider.

3

Conclusions

References

River water samples taken upstream
and downstream from a water treatment
plant were analyzed using an Agilent
6550 iFunnel Q-TOF LC/MS and a suiteof Agilent MassHunter statistical andqualitative analysis software packages.
The result was an untargeted screening
method that detected more than 890
unknown compounds. Compounds ofinterest were submitted for identiﬁcation
using accurate mass databases resulting
in 150 suggested contaminants. MS/MS
analysis with library matching identiﬁed
21 contaminants, and 32 additional
contaminants were conﬁrmed withauthentic standards. For the compounds
which were not present in the databaseor library, in silico fragmentation wasused, along with a ChemSpider search.
These results will be used to further
expand compounds present in theaccurate mass database and library for
future studies.

1. M. Gust, et al. “Effects of Short-Term
Exposure to Environmentally
Relevant Concentrations of Different
Pharmaceutical Mixtures on the
Immune Response of the Pond Snail
Lymnaea Stagnalis” Science of the
Total Environment 445–446(0): 210-218(2013).
2. R.H. Triebskorn, et al. “Ultrastructural
Effects of Pharmaceuticals(Carbamazepine, Cloﬁbric Acid,
Metoprolol, Diclofenac) in Rainbow
Trout (Oncorhynchus Mykiss) andCommon Carp (Cyprinus Carpio)”
Analytical and Bioanalytical Chemistry387(4): 1405-1416 (2007).
3. B.I. Escher, et al. “Most Oxidative
Stress Response in Water Samples
Comes from Unknown Chemicals: The
Need for Effect-Based Water Quality
Trigger Values” Environmental Science& Technology (2013).
4. M. Zedda and C. Zwiener“Is Nontarget Screening of Emerging
Contaminants by Lc-Hrms Successful?
A Plea for Compound Libraries andComputer Tools.” Analytical andBioanalytical Chemistry 403(9):2493-2502 (2012).

www.agilent.com/chem
This information is subject to change without notice.
© Agilent Technologies, Inc., 2014
Published in the USA, February 27, 2014
5991-3967EN

Screening for Emerging Chemical
Contaminants in Water Using
LC/Q-TOF and Mass Profiler
Professional Software
Application Note
Environmental

Authors

Abstract

Sylvain Merel and Shane Snyder

The Agilent 6540 Q-TOF LC/MS and Agilent Mass Profiler Professional Software

Department of Chemical and(MPP) were used to sensitively characterize the complex chemical composition ofEnvironmental Engineeringmunicipal wastewater. The processing of Q-TOF high-resolution MS data using the

University of Arizona

MPP multivariate statistical analysis package revealed changes in the occurrenceTucson, AZ

patterns of organic chemicals during water treatment. Using this technique, weUSAwere able to determine which organic contaminants were attenuated, resilient, andformed during ozonation of recycled water. Heat maps created in MPP provide patterns that can be used to assess subtle changes in water quality and to identify
emerging contaminants unique to a particular water or treatment process.

Introduction

Experimental

A plethora of reports have shown trace levels of unregulated
contaminants (aka emerging contaminants) in water supplies[1] and in drinking water [2]. Those chemicals detected represent a very small portion of the approximately 7,500 chemicals
listed in the US Environmental Protection Agency (EPA)
Contaminant Candidate List universe (http://water.epa.gov/
scitech/drinkingwater/dws/ccl/ccl3.cfm) and an evensmaller portion of the nearly 85 million chemicals assigned
CAS numbers (https://www.cas.org/). Numerous chemicals
exist in water that have not yet been identified, and largely
have an unknown impact on environmental and public health.

Reagents and standards
All solvents used were of highest purity available and suitablefor LC/MS analysis. Methyl tertiary butyl ether (MTBE),
methanol, and HPLC grade water used for solid phase extraction were procured from Fisher Scientific. Acetonitrile and
HPLC grade water used for chromatography were obtained
from Burdick and Jackson, while the additive formic acid wasprocured from Sigma-Aldrich.

Instruments
This study was conducted using an Agilent 1290 Infinity LC
System coupled to an Agilent 6540 Ultra High Definition(UHD) Accurate-Mass Q-TOF LC/MS system. The instrument
conditions are shown in Table 1.

Specifically, regional water resources are facing unprecedented stress due to water shortages resulting from rapid
population growth and relocation [3]. Many metropolitan
areas are seeking additional resources to augment drinking
water supplies [3]. The most likely new resource is waterreuse, involving recycling wastewater into drinking water.
Considering the daunting number of chemicals identified andpotentially present in municipal wastewater, monitoring programs struggle to select those indicator compounds that are
most representative of treatment efficacy. Perhaps more difficult is the nearly unlimited number of transformation productsthat may form when organic contaminants are subjected tooxidative or biological treatment processes. In some cases,
byproducts of treatment are more toxic than the initial contaminants [4]. A survey method is needed that can monitor allof these processes to ensure selection of the most efficientand cost-effective treatments for wastewater reuse.

Table 1. LC and Q-TOF MS Conditions
LC run conditions
ColumnAccurate-mass quadrupole time-of-flight (Q-TOF) spectrometry is an excellent platform for detecting and resolving tracelevels of thousands of different organic compounds in water,
making it ideal as a monitoring tool for wastewater treatment.
This application note describes a method that has been developed to profile the organic contaminants in wastewater
before and after ozonation, a technology commonly used fordisinfection and organic contaminant attenuation. The
method developed employs ultra high performance liquid
chromatography (UHPLC) and accurate-mass spectrometry,using an Agilent 1290 Infinity LC System and an Agilent 6540
Q-TOF LC/MS to separate and detect thousands of organic
compounds in water. Clusters of compounds with similar fate
during a given treatment process were elucidated, and someof these compounds could be used as indicators of the efficiency of the oxidation process. Finally, should a contaminantbecome of particular interest in the future, the Q-TOF data will
constitute an archive that could be mined again, providing
historical data without the necessity of storing or reanalyzing
any sample.

Agilent ZORBAX Eclipse Plus 50 × 2.1 mm, 1.8 µm(p/n 959757-902)

Column temperature

35 °C

Injection volume

3 µL (three replicate injections of each sample)

Mobile phaseA) 0.1% formic acid in water v/v
B) 0.1% formic acid in acetonitrile v/v

Linear gradientTime (min)

%A

%B

0

95%

5%

1.5

95%

5%

10

0%

100%

13

0%

100%

Flow rate

0.4 mL/min

Q-TOF MS conditions

2

Ion mode

ESI positive, MS only

Nebulizer gas

40 psiCapillary voltage

4,000 V

Acquisition mode

2 GHz

Acquisition range

25–3,200 m/z

Acquisition speed

2.5 spectra/sec

Resolving power

20,000 at m/z = 400

Accuracy< 2 ppm

Sample preparation

tool in MassHunter, followed by a second round of compoundalignment in MPP. Statistical analysis to identify profile
differences between sample types was also performed using
MPP.

Samples were collected from a wastewater treatment plant
located in Arizona, where some of the secondary wastewater
effluent was partially diverted through an ozonation pilot.
Samples were collected after treating the secondary
wastewater with 0, 1.5, 3, 4.5, or 5.6 mg/L of ozone.

Results and Discussion

Each sample was then filtered through a glass fiber filter(Whatman GF/F). Solid phase extraction was performed using
cartridges preconditioned with 5 mL of MTBE, 5 mL of
methanol and 5 mL of reagent grade water. The water sample(375 mL) was loaded onto the cartridge, which was thenrinsed with 5 mL of reagent grade water and dried for
30 minutes under a nitrogen stream. The cartridge was thensequentially eluted with 5 mL of methanol and 5 mL of
methanol/MTBE (10/90). The eluate was evaporated down to
0.5 mL under a nitrogen stream.

Feature extraction and compound alignment
While total ions chromatograms (TICs) of triplicate injections
overlap, and some differences can be observed corresponding
to the different levels of ozone treatment, they are too complex for direct interpretation (Figure 1). Feature extraction isnecessary to extract compounds from each chromatogramand establish a detailed profile of each sample. However,between two injections, a compound might have an insignificant shift in retention time that would cause MassHunter to
identify it as two different entities. To correct for this, afterfeatures were extracted in MassHunter they were subjected
to compound alignment using MPP. This process identified a
total of 24,779 compounds. Initial filtering by occurrence(blank subtraction) identified 23,574 compounds detected only
in wastewater samples. Of these, 13,996 compounds weredetected at least twice (either in different replicates or indifferent samples), and were used for recursive analysis tofurther eliminate any false positives.

Data analysis
The data were processed using Molecular Feature Extractor(an algorithm detecting compounds based on isotopic ion
clusters) in the Agilent MassHunter software suite, followedby compound alignment using the Agilent Mass Profiler
Professional (MPP) multivariate statistical analysis package.
To discard artifacts, the aligned features were then subjected
to recursive analysis using the Find Compound by Formula

Counts×107
0Counts×107
1Counts1

Ozonated wastewater(3 ppm)0

×107
CountsOzonated wastewater(1.5 ppm)

0

×107

1

Ozonated wastewater(4.5 ppm)

0×107
Counts

Untreated wastewater

1

Ozonated wastewater(5.6 ppm)

0
0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

9.0

9.5

10.0

Acquisition time (min)Figure 1. Total ions chromatograms (TICs) for untreated wastewater and wastewater treated with various doses of ozone.

3

10.5

11.0

11.5

12.0

12.5

Recursive analysis

Principal component analysis

The 13,996 compounds that passed the initial filtering were
exported in a CEF file and imported into MassHunter
Qualitative Analysis Software. The chromatograms were
examined again to search particularly for these compounds
using the Find Compound by Formula tool, and then the
resulting compounds were imported back into MPP for asecond round of compound alignment and filtering. A total of
12,889 compounds passed this recursive analysis. Of these,
only those compounds that did not appear in blanks(extracted HPLC grade water) and were detected in 100% of
the triplicate analyses for at least one of the ozone doselevels were selected for statistical analysis, leaving a total of
9,493 compounds.

Principal Component Analysis (PCA) was performed on thedata using MPP to determine if the samples could be distinguished based on the ozone dose that they received. Figure 2
shows that every ozone level could be distinguished from theothers, and the triplicate samples taken at each level cluster
very closely, indicating excellent reproducibility.

10,0000-10,000 -20,000

Y-axis-4,000 -2,0000

Hierarchical clustering analysis
To further characterize and clearly observe the impact ofozonation on compounds present in wastewater, Hierarchical
Clustering Analysis (HCA) was performed on the samples,
revealing several groups of compounds that differed in their
response to ozonation (Figure 3). Two groups, (A and B) were
removed by ozone, while three others (C, D, and E) wereformed by ozone treatment. Several compounds were actuallyresistant to ozone treatment (F). Additional detail can beobserved in the HCA. For example, differences in the level ofozone required to remove compounds can be seen inGroup B, revealing three subgroups (B1, B2, and B3, Figure 4).

1,000 2,000 3,000 4,000

5,000

5,000

0

0

Pilot (0 mg/L ozone)
Pilot (1.5 mg/L ozone)
Pilot (3 mg/L ozone)-5,000-5,000

Pilot (4.5 mg/L ozone)
Pilot (5.6 mg/L ozone)-10,000-10,000

Z-axis-30,000

X-axis-4,000-20,000-10,000-2,00000

2,000
10,0004,000

Figure 2. PCA reveals that samples treated with different doses of ozone can be clearly differentiated. The triplicates in each sample group also cluster very
closely together.

4

Increasing ozone dose
0 mg/L

1.5 mg/L

3 mg/L

4.5 mg/L

5.6 mg/L

F
A and B:
Compounds removed by ozone

AC
C, D and E:
Compounds formed by ozone

F:
Compounds resistant toozone treatment

D

B

E

Color range

Molecular feature abundance
normalized intensity (Log2)

-11.2

0

11.2

Figure 3. HCA, revealing groups of compounds that respond differently to ozone treatment.

Increasing ozone dose
0 mg/L

1.5 mg/L

3 mg/L

4.5 mg/L

5.6 mg/L
B1: Compounds removed by
4.5 mg/L ozone or more
B2: Compounds removed by
4.5 mg/L ozone or more
B3: Compounds removed by
1.5 mg/L ozone or more

Figure 4. Expanded view of Group B from Figure 3, showing the three subgroups (B1, B2, and B3) that differ in the level of ozone required to remove them fromthe wastewater.

5Analysis of variance

Identification of compounds

To further understand the impact of various ozone treatments
on wastewater, an analysis of variance (ANOVA) was performed in combination with Tukey’s test to identify thosecompounds whose abundance varied significantly between
the various ozone doses applied. The ANOVA identified 8,244
compounds out of 9,493 that had a significant variation ofabundances (p value <0.05) dependent on the ozone dose.
Tukey’s test provided a table comparing each sample treatedby a different ozone dose, indicating the number of compounds that did or did not significantly vary in abundance(Table 2).

It is not feasible to monitor all compounds in water to assess
their removal. Therefore, an alternative is identifying one
compound in each cluster (B1, B2…) that could be used as anindicator to assess the fate of the other chemicals with similar behavior. This can be done using ID Browser in MPP and a
database of suitable compounds. For example, when randomly selecting a compound in the subgroup B3, the Find
Similar Entities tool in MPP identified 953 compounds that
had a similar fate during ozonation with a correlation coefficient 0.95 < R2 < 1. This list was exported to ID Browser,
which searched the Agilent METLIN Personal Compound
Database and Library (PCDL) for matches. One of the compounds was identified as warfarin, the most widely prescribed
anticoagulant drug in North America (Figure 5). Therefore, it
could be used as an indicator for the fate of the 952 othercompounds that are also easily removed by ozone(subgroup B3).

Table 2. Number of Compounds That Vary in Abundance with Ozone Dose(ANOVA with p value < 0.05)

Ozone dose (ppm)

Ozone dose (ppm)
0

1.5

3.0

4.5

5.6

0

8244

4548

5524

6083

6150

1.5

3736

8244

3083

4299

4843

3.0

2720

5161

8244

2476

3395

4.5

2161

3945

5768

8244

2226

5.6

2094

3401

4849

6018

8244

Compounds that vary in abundanceCompounds that do not vary in abundanceComparison of one sample group to itself

×10 4
4

309.1113(M+H) +

Warfarin
C19H16O 4

3.5

O

O

3

CH3
OH

Counts

2.5

O

2
1.5
310.1148(M+H) +

1

311.1217(M+H) +

0.5
0
309

309.5

310

310.5
311
Mass-to-charge (m/z)

311.5

312

312.5

Figure 5. One of the compounds in subgroup B3 identified as warfarin using MPP ID Browser and the Agilent METLIN PCDL database. The identification score
was 97 (of a maximum of 100), and the red rectangles represent the theoretical ion cluster for warfarin.

6

Conclusions
Wastewater contains a very large number of compounds,
most of which are not monitored in routine analysis. Using
untargeted LC/Q-TOF analysis on the Agilent 6540 Ultra HighDefinition (UHD) Accurate-Mass Q-TOF LC/MS system andstatistical analysis with Agilent Mass Profiler Professional
software can differentiate groups of compounds with different
vulnerability to ozone. Some compounds are resilient toozonation, others are more or less easily attenuated depending on the ozone dose and, finally, some are formed by ozonation. Therefore, the method presented in this application notecan be used as a sensitive monitoring tool for the changes inwater quality that occur during water treatment.

References
1. D.W. Kolpin, et al. “Pharmaceuticals, hormones, and otherorganic waste contaminants in U.S. streams, 1999-2000: anational reconnaissance” Environ. Sci. Technol. 36,
1202-1211 (2002).
2. M. Benotti, et al. “Pharmaceuticals and endocrine disrupting compounds in U.S. drinking waters” Environ. Sci.
Technol. 43, 597-603 (2009).
3. R.G. Arnold, et al. “Direct potable reuse of reclaimed
wastewater: it is time for a rational discussion”
Rev. Environ. Health 27, 197-206 (2012).
4. National Research Council “Water Reuse: Potential for
Expanding the Nation's Water Supply Through Reuse of
Municipal Wastewater “ Washington, DC: The National
Academies Press, 2012.
5. K. Ikehata, M. Gamal El-Din, S.A. Snyder “Ozonation andadvanced oxidation treatment of emerging organic pollutants in water and wastewater” Ozone-Science &Engineering 30, 21-26 (2008).

For More Informatiom
For more information on our products and services visit our
Website at www.agilent.com/chem.

7

www.agilent.com/chem
Agilent shall not be liable for errors contained herein or for incidental or consequential
damages in connection with the furnishing, performance, or use of this material.
Information, descriptions, and specifications in this publication are subject to change
without notice.

© Agilent Technologies, Inc., 2014
Published in the USA
April 24, 2014
5991-4417EN

Screening and Identificationof Emerging Contaminants in
Wastewater Treatment Plant
Effluents Using UHPLC/Q-TOF MS
and an Accurate Mass Database andLibrary
Application Note
Environmental, Emerging contaminants, Water,
Accurate mass screening, pesticides, PPCPs

Authors

Abstract

Jean Daniel Berset

This application note describes the creation of an accurate mass library for

Water and Soil Protection Laboratory

relevant environmental contaminants and its application for the screening of(WSPL), Bern, Switzerland

pesticides, pharmaceuticals and personal care products, their metabolites, andEmma Rennie
Agilent Technologies, Inc., Santa Clara,
CA, USA
Thomas GlaunerAgilent Technologies GmbH,
Waldbronn, Germany

transformation products in effluents of municipal waste water treatment plants(WWTPs). An Agilent 1290 Infinity LC System coupled to an Agilent 6550 iFunnel
Q-TOF LC/MS System was operated in positive and negative electrospray modeusing Dual Spray Agilent Jet Stream Technology. Accurate mass spectra wereacquired for a large collection of potential environmental contaminants in either
one or both ionization modes, and for all relevant ion species.
Four WWTPs in central Europe were sampled over 3.5 months, and samples
were analyzed using a Target Screening approach including reference standards
for 390 contaminants. In addition, an extended suspect screening was doneusing all compounds included in the Agilent MassHunter Water Screening
Personal Compound Database and Library (PCDL) as the suspect list. Samples
were measured using All Ions MS/MS as well as auto MS/MS acquisition withan inclusion list. The results clearly show the value of both acquisition modes
combined with an efficient data analysis workflow, as well as the inherent
value of the Water Screening PCDL for the surveillance screening of potential
contaminants in complex environmental samples.

Introduction

large volume direct injection of water samples. For most of
the LC/MS amenable compounds specified in the EU Water
Framework Directive, the EU Drinking Water Directive, or
the EPA Clean Water Act, method detection limits in the low
ng/L range can be achieved with this type of Q-TOF LC/MS
method [8].

Environmental regulations throughout the world currently
focus on monitoring a limited number of well-known
compounds that are assumed to be responsible forsignificant ecological and human health related risks [1].
As a consequence, there is a tendency to further reduce themaximum allowable concentrations of the environmental
quality standards (EQS) for those priority pollutants. In
Europe, for example, EU directive 2013/39/EU specifies EQS
for 41 priority substances or substance groups amongst
which are pesticides, perfluorocarbons (PFCs), nonyl- andoctylphenol, polybrominated flame retardants, dioxins
and PCBs, polyaromatic hydrocarbons (PAHs), and heavy
metals [2]. However, these priority pollutants represent
only a small fraction of the anthropogenic chemicals thatare used, and consequently found in the environment.
Apart from the legal requirement for surveillance screening
for potential contaminants in surface waters in the EU,
there is a growing interest to collect occurrence data forcontaminants of emerging concern. This is reflected in a
number of country‑specific government regulations, such as
the EPA method 1694, on the analysis of pharmaceuticals
and personal care products in water, soil, and sediments [3],and in the US EPA Contaminant Candidate List (CCL) fourth
edition from 2015 [4].

Three different approaches towards substance identification
are used in the environmental context in combination withaccurate mass LC/MS: Target, Suspect, and Non-target
Screening.
In Target screening, a reference standard is measured withthe same analytical method and within the same worklist as
the unknown sample, so that retention time, accurate mass,and fragment information can be directly compared, and(semi-)quantitative results are obtained.
Suspect screening describes a workflow where compound
database and MS/MS library information of expectedcontaminants are used to screen for the suspected
substances. While no reference standard is available,
identification of compounds with increasing confidence isobtained by comparing accurate mass and isotope patterns,
retention times, and coeluting fragments, or by a spectral
MS/MS library comparison.
Non-target screening aims at the identification of allremaining components detected in a sample where no
structural information is available. Often, non-target screening
includes the statistical comparison of two or more samples
or sample groups for data reduction. A full identificationconsiders the accurate mass, isotope pattern, andMS/MS spectrum information, and includes spectral librarycomparison for known compounds, and spectral similarities
and in-silico fragmentation tools such as Agilent MassHunter
Molecular Structure Correlator (MSC) for unknowns.

Most anthropogenic contaminants enter the aquatic
environment as a result of incomplete removal in wastewater
treatment plants (WWTPs). At the same time, degradationand transformation products are formed during biologicalwastewater treatment. These products are typically notmonitored, and often are not even known [5,6]. Surface
waters downstream of WWTPs contain innumerable
anthropogenic contaminants and their transformation
products at trace levels. When river water is subsequently
used as the source for drinking water production, referred toas unplanned indirect potable reuse, comprehensive data on
the chemical quality of the water are essential. This becomeseven more important as planned indirect and direct potablereuse becomes a common practice [7] due to climate change,
population growth, and water scarcity.

Compound databases containing accurate mass MS/MS
spectral data for all expected contaminants, as well as thosetheoretically predicted, and their transformation products
have been proven to assist in the identification of potentiallyrelevant compounds and support efficient data analysis in all
three screening workflows. The Agilent MassHunter Water
Screening Personal Compound Database and Library (PCDL)
contains a relevant list of more than 1,400 environmental
contaminants including pesticides, pharmaceuticals, personalcare products, large volume industrial chemicals, and theirtransformation products. This PCDL includes all compounds
currently regulated in the US, EU, and China, and further
compounds that have either been previously detected in
the environment or are likely to be detected due to theirproduction amount or widespread use.

Targeted analytical methods are increasingly complemented
by untargeted acquisition methods using high resolution
accurate mass Q-TOF LC/MS due to comprehensive screening
requirements in current environmental regulations as wellas increasing interest in the occurrence of contaminants ofemerging concern. Agilent Q-TOF LC/MS instruments allow
for full spectrum acquisition with high sensitivity and at avery high data rate. This is essential to obtain information onmolecular ions, isotope patterns, and fragments with a single
2

Experimental

Searchable fields containing compound information including
compound class and regulation tags make it easy to create
subsets of the Water Screening PCDL for target and suspect
screening. The Water Screening PCDL contains more than
1,000 compounds with accurate mass MS/MS spectra, which
directly supports the Agilent All Ions MS/MS workflow
for Target and Suspect screening, and greatly increases
confidence in the identification of potential contaminants [9].

Reagents and standards
All reagents and solvents were HPLC or LC/MS grade.
Acetonitrile, methanol, and acetic acid were purchasedfrom Fluka (Sigma-Aldrich, Buchs, Switzerland). Ammonium
acetate was purchased from VWR International (Darmstadt,
Germany). Ultrapure water was produced using a Milli-Q
Integral system equipped with a 0.22-µm point-of-use
membrane filter cartridge (EMD Millipore, Billerica, MA,
USA). For acquisition of accurate mass MS/MS spectra,
most individual reference compounds were purchasedfrom Sigma-Aldrich (Buchs, Switzerland) or from VWR
International (Darmstadt, Germany). Reference standards fortransformation products and residues were purchased from
Ehrenstorfer (LGC Standards, Wesel, Germany). Estimationof retention times was performed using mixed standard
solutions of pesticides, pharmaceuticals, and drugs of abuse,
which were part of the LC/MS Pesticide Comprehensive
Test Mix (p/n 5190-0551), the LC/MS Forensic Toxicology
Comprehensive Test Mix (p/n 5190-0555), or were provided byresearch groups.

This application note describes the screening and(semi-)quantification of contaminants in WWTP effluentsusing an Agilent 1290 Infinity UHPLC coupled to the highly
sensitive Agilent 6550 iFunnel Q-TOF LC/MS system.
Four WWTPs located in agricultural and rural areas in
Switzerland were sampled over 3.5 months, covering themain pesticide application period from March to late June.
Comprehensive screening of potential water contaminants
revealed the chemical characteristics of the different
treatment and different catchment areas. A subset of theWater Screening PCDL, containing roughly 390 compounds,
was used for Target screening and (semi-)quantification.
For Suspect screening, the entire comprehensive Water
Screening PCDL was used to find and identify compoundswith high confidence. Examples of the two different screening
strategies used are shown. First, the Q-TOF was operated in
the All Ions MS/MS mode with three collision energies. The
All Ions technique features an easy setup of the acquisition
method with verification of the potential contaminants using
the MS/MS spectral library to produce chromatographic
coelution of the precursor and product ions. An additional
approach was used for compounds for which either no library
spectrum was available, or which were not verified due to a
low coelution score. Their precursor masses were added to
an inclusion list for an Auto MS/MS method using a second
injection. The obtained spectra were compared to the MS/MS
library for compound identification. This workflow can also
be combined with a discovery run operated in TOF mode toobtain the target list of suspects.

Stock standard solutions were prepared by dissolving thereference compounds in isopropanol, acetonitrile, methanol,
water, or mixtures thereof, depending on the physicochemical
properties of the substance. For calibration and spiking
experiments, the comprehensive test mixtures for pesticides,
pharmaceuticals, and drugs of abuse were combined to a
multi-analyte working solution. Stock standard solutions aswell as the multi-analyte working solution were stored until
use at –20 °C. Calibration samples were prepared by dilutionof the working solution with tap water.

Sample preparation
Effluents of four different wastewater treatment plants incentral Europe were collected as 14-day composite samples
over 3.5 months (March to June). The catchment areas ofthree of the WWTPs are agriculturally dominated (AG, AI,
AL), whereas the other one is located in an urban area (AZ).
One of the WWTPs also receives some industrial wastewater(AL). Efficient nitrification-denitrification is observed in threeof the WWTPs (AG, AL, AZ) and, thus, a better elimination of
trace contaminants can be expected.
Samples were filtered using glass fiber filters, and werestored at –20 °C. Immediately before measurement, samples
were thawed, and an aliquot was transferred to an HPLC vial.

3

LC/Q-TOF MS analyses
Separation was carried out using an Agilent 1290 Infinity
UHPLC system consisting of:• Agilent 1290 Infinity Binary Pump (G4220A)• Agilent 1290 Infinity High Performance Autosampler(G4226A) equipped with a large volume injection kit(G4216-68711)

Table 1.

Chromatographic Conditions

Parameter

Value

UHPLC column

Agilent ZORBAX RRHD SB-Aq, 2.1 × 150 mm, 1.8 μm(p/n 859700-914)

Column temperature

40 °C

Mobile phaseA) 1 mM NH4 acetate + 0.1% acetic acidB) 0.1% acetic acid in acetonitrile

Gradient programTime (min)
0.0
2.0
14.0
16.0
19.0
19.5

Stop time

20.0 minutes

Post time

3.0 minutes

Flow rate

0.40 mL/min

Injection volume

100 μL• Agilent 1290 Infinity Thermostatted Column compartment(G1316C)
The UHPLC system was coupled to an Agilent G6550A
iFunnel Quadrupole Time-of-Flight LC/MS System equippedwith a Dual Spray Agilent Jet Stream electrospray ionization
source. The Q-TOF LC/MS instrument was operated with:• Agilent MassHunter Acquisition rev. B.06.01 in 2 GHz
extended dynamic range mode with positive or negativeelectrospray ionization (ESI) with two different methods:
•
•

%B
0
0
98
98
0
0

All Ions MS/MS acquisition with 3 scans/sec withtwo discrete collision energies

Table 2.
Parameter

Value

Auto MS/MS acquisition with a data rate of
5 scans/sec in MS and 5 scans/sec in MS/MS

Gas temperature

160 °CGas flow

16 L/min

Nebulizer

30 psig

Sheath gas temperature

350 °C

Sheath gas flow

12 L/minThe use of two collision energies in the All Ions acquisition
resulted in alternating spectra with a low energy channel
containing the precursor ion, and two high energy channels
containing the precursor and product ions.
Reference mass ions were delivered using an Agilent Infinity
1260 Isocratic pump (G1310B) operated with a flow rate of
1.0 mL/min, and using a 1-in-100 flow splitter (G1607‑60000).
The final flow rate going to the reference sprayer was
10 µL/min.
Table 1 shows the chromatographic conditions, and Table 2
shows the major MS conditions.
Data were evaluated using MassHunter Qualitative
Analysis software B.07.00. Positive identifications of watercontaminants were reported if the compound was detectedin the accurate mass MS data by the Find-by-Formula data
mining algorithm with a mass error below 5 ppm, and witha sufficient score (including isotope abundance and isotope
spacing). A retention time window of ±1 minute was specified
for peak detection to compensate for retention time shifts due
to matrix variability.

Major MS Conditions

Positive

Negative

Capillary voltage

4,500 V

3,500 V

Nozzle voltage

500 V

1,000 V

Reference mass correction

121.05087
922.00980

112.98559
1,033.98811

All ions MS/MS
Mass range

50 to 1,200 amu

Scan rate

3 spectra/s

Collision energies

0–20–40 V

Auto MS/MSMS Mass range

100 to 1,200 amu

MS/MS Mass range

50 to 1,200 amu

Scan rate

5 spectra/s (MS)
5 spectra/s (MS/MS)

Collision energy

20 V

4

Creation of the Agilent MassHunter Water
Screening PCDL

corrected to their theoretical masses. All MS/MS spectra
were curated for spectral noise, and a minimum base peakthreshold was applied to ensure good ion statistics for all
fragment ions. The corrected spectra were included in the
Agilent Water-Screening Personal Compound Database andLibrary (G6882CA).

Accurate mass spectra of single-analyte solutions wereacquired with flow injection or using a short column intargeted MS/MS mode with collision energies of 10, 20,
and 40 V. If precursor ion stability required higher collisionenergies, additional spectra were acquired in a second run.
Typically, MS/MS spectra were acquired for the [M+H]+
and [M-H]– ion species for each analyte. If highly abundant
additional adduct ion species were observed, accuratemass MS/MS spectra were also acquired for the [M+NH4]+,[M+Na]+, or [M+Cl]– species. In either positive or negative
ionization mode, meaningful MS/MS spectra were acquired
for more than 1,000 relevant water contaminants. For many
compounds, MS/MS library spectra were captured in both
ionization modes, and for more than one precursor ion
species. To eliminate mass assignment errors, fragment
masses in the acquired spectra were compared to thetheoretical fragment formulas, and all ion peaks were

Figure 1.

The Water Screening PCDL was then used for the screening
and identification of environmental contaminants in theeffluents of four WWTPs. Moreover, by analyzing mixed
standard solutions of pesticides, pharmaceuticals, anddrugs of abuse with the given UHPLC method, retention
time information was added to 390 compounds, thus adding
in retention time to the identification score and increasing
identification confidence.
Figure 1 shows a screen capture of the MassHunter PCDLManager software along with the accurate mass MS/MS
spectrum of the antiviral drug aciclovir acquired in negative
ionization mode with a collision energy of 20 V.

Agilent MassHunter PCDL Manager software showing the Agilent MassHunter Water Screening PCDL and the accurate mass MS/MS
spectrum of aciclovir acquired in negative ionization mode with a collision energy of 20 V.

5Results and Discussion

MS/MS software workflow, fragment ions that show perfectcoelution with the molecular ions were identified and theideal collision energies and relative ratios were detected.
This information was passed to the MassHunter Quantitative
Analysis software for (semi-)quantification and batch review
using accurate mass for molecular ions and fragments as well
as isotope pattern matching for compound identification withhigh confidence.

Target screening for pesticides, pharmaceuticals,
and personal care products
For the target screening and (semi-)quantification ofpesticides, pharmaceuticals, and personal care products inthe WWTP effluents, samples were measured in positive andnegative All Ions MS/MS mode with collision energies of 0,
20, and 40 V. A subset PCDL with 390 entries was createdfrom the Water Screening PCDL including all compounds
for which reference standards were available. Data forthe calibration samples were initially evaluated in the
MassHunter Qualitative Analysis Software (B.07.00) using
the Find-by‑Formula data mining algorithm with a mass errorof ± 5 ppm and a retention time window of ± 0.5 minutes
compared to the expected retention time. In positive mode,[M+H]+ and [M+NH4]+ species, and in negative mode, [M-H]–
species have been considered as charge carriers since they
are represented by the majority of accurate mass MS/MS
spectra included in the PCDL. Identification was done by
Fragment Confirmation using the Water Screening PCDL
as the fragment ion source, and evaluating the five most
specific ions from the MS/MS spectral library. In the All Ions

From the 390 targeted compounds, 315 were detected inpositive mode, and 75 were detected in negative mode. Witha direct injection of 100 µL of water into the UHPLC Q-TOF MS
system, more than 60% of the compounds could be quantified
at or below 10 ng/L in the spiked tap water samples. Another
35% of the compounds were detected between 10 and
100 ng/L, and just 5% of the compounds were only detected
at concentrations of 200 ng/L or higher. For most targetedcompounds, one or more specific fragment ions could be
used as qualifier ions, and generally mass accuracy of themolecular ions and fragments was better than 5 ppm. Figure 2
shows the extracted ion chromatograms, MS peak spectra
and calibration curves for some examples from the lists ofpriority pollutants (EU) and US EPA method 1694.

6Ibuprofen
30 ng/L

2.8
2.4
2.0
1.6
1.2
0.8
0.4
0
0

400

1,200
800
1,600
Concentration (ng/L)

Ibuprofeny = 192.666716*x – 64.061529– Scan (12.253 – 12.385 min, 8 scans)×10 4 Mass Match Score = 86.3 (M-H) –×10 5 R 2 = 0.9989
6
3.6
205.1234
3.2
5(M-H) –
2.8
206.1268
4
–
(M-H)
2.4
207.1294
2.0
3(M-H) –
1.6
2
1.2
1
0.8
0.4
0

12.0
12.4
Acquisition time (min)0
205 206 207 208
Mass-to-charge (m/z)

0500

1,000 1,500 2,000 2,500 3,000
Concentration (ng/L)

Responses

308.0040, 196.9789, 213.9821+ Scan (14.307-14.417 min, 6 scans)+
×10 Ratio = 33.8 (102.7 %)×10 5 Mass Match Score = 91.0 (M+H)
2.8 Ratio = 16.0 (95.4 %)1.2
2.6
308.0040
1.1
2.4(M+H) +
Quinoxyfen
1.0
2.2
20 ng/L
2.0
0.9
309.9996
1.8
0.8
1.6
0.7
1.4
0.6
1.2
0.5
1.0
0.4
0.8
0.3
0.6
0.4
0.2
0.2
0.1
0
0
13.8 14.2 14.6
307 309 311
Acquisition time (min)Mass-to-charge (m/z)
CountsCounts4

Quinoxyfen
y = 7035.818343*x – 14860.205250
×10 R 2 = 0.9998
3.6
3.2
2.8
2.4
2.0
1.6
1.2
0.8
0.4
0
6

0

50 100 150 200 250 300 350 400 450 500 550
Concentration (ng/L)

EIC chromatograms of molecular ion and fragments, MS peak spectra and calibration curves for erythromycin (positive mode),
ibuprofen (negative mode), diclofenac (negative mode), and quinoxyfen (positive mode).

7

2,000

Responses

CountsCountsDiclofenac
y = 557.652582*x – 3028.541575
294.0094, 250.0196, 214.0429– Scan (12.386 – 12.512 min, 7 scans)×10 6 R 2 = 0.9995
×10 3 Ratio = 44.3 (88.2 %)×10 5 Mass Match Score = 83.8 (M-H) –1.1
Ratio = 11.6 (91.7 %)1.2
4.0
294.0094
1.0
1.1
–
(M-H)
3.5
1.0
0.9
Diclofenac
0.9
0.8
3.0
296.0067
40 ng/L
0.8
0.7(M-H) –
2.5
0.7
0.6
0.6
0.5
2.0
0.5
0.4
1.5
0.4
0.3
295.0126
298.0043
1.0
0.3(M-H) –
0.2(M-H) –
0.2
0.1
0.5
0.1
0
0
0
12.0 12.4 12.8
294 296 298
0
1,200
400
800
1,600
Acquisition time (min)Mass-to-charge (m/z)
Concentration (ng/L)

Figure 2.

2,000

Responses

Counts205.1234, 161.1336
Ratio = 27.6 (100.0 %)Erythromycin
y = 1711.201929*x – 30380.252071
×10 6 R 2 = 0.9908
3.2

Responses

Counts

×10
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
3

CountsCounts734.4685, 158.1176 , 576.3742+ Scan (8.914–9.079 min, 9 scans)
Ratio = 95.5 (104.4 %)Mass Match Score = 88.3 (M+H) +×10 3 Ratio = 52.4 (110.1 %)×10 4
734.4685(M+H) +
3.6
7
3.2
6
Erythromycin
2.8
40 ng/L
5
2.4
4
2.0
1.6
3
1.2
2
0.8
1
0.4
0
9.0
8.6
9.4
733 735 737 739
Mass-to-charge (m/z)
Acquisition time (min)Applying this target screening method to the samples fromthe WWTP effluents allowed the (semi-)quantificationof a large variety of environmental contaminants in aconcentration range of a few ng/L up to several µg/L.
Pharmaceutical residues were found to dominate theeffluents of the larger WWTPs (AG, AL, AZ), which all receive
wastewater from about 50,000 population equivalents, while
AI receives wastewater from 7,100 population equivalents.
In all wastewater treatment plant effluents, X-ray contrast
media were identified with estimated concentrations of up
to 7 µg/L for iomeprol, and up to 2 µg/L for iopromide. Other
common pharmaceuticals were amisulprid (up to 500 ng/L),
atenolol (up to 1.7 µg/L), metoprolol (up to 470 ng/L), andtramadol (up to 2 µg/L) as well as carbamazepine, diclofenac,
ibuprofen, naproxen, and sulfamethoxazole. Metabolites
were identified for carbamazepine, sulfamethoxazole, andmetamizole. In total, 33 pharmaceuticals and metabolitesof pharmaceuticals were identified in the WWTP effluents.
As expected, the largest number of these compounds were
found in the effluent of AZ, which has an urban catchment
area. However, concentrations were highest in effluents ofAI, probably due to the limited elimination efficiency of thesimple treatment.

AI-1 [Carbamazepine] AI-2 [Carbamazepine]
AI-3 [Carbamazepine]
237.1022 Area=1,018,313 237.1022 Area=1,124,201
194.0964 Area=1.2e06 194.0964 Area=971,732 194.0964 Area=1,076612
192.0808 Area=2.2e06
192.0808 Area=2.7e06 192.0808 Area=2.1e06

×10 5 237.1022 Area=1.3e07
3.6

In the effluents of all WWTPs, a total of 46 pesticides weredetected. The insecticide diethyltoluamide (DEET) was
found in all WWTPs with a concentration of 14 to 770 ng/L.
Also present in samples of all WWTPs were the herbicides
metolachlor (up to 1.1 µg/L) and isoproturon (up to 450 ng/L).
The largest number of pesticides were found in the effluentof AI, which has an agriculturally-dominated catchment area.
Major crops grown there include cereals, vegetables, corn,
beetroot, and potatoes. The pesticides found most often, andwith the highest concentrations in the effluent of this WWTP
were azoxystrobin, flufenacet, linuron, metamitron, methomyl,
metribuzin, propamocarb, spiroxamin, and terbuthylazine. Allof these pesticides are mainly used for the above listed crops.
Figure 3 shows the normalized chromatograms for theantiepileptic drug carbamazepine and the pesticide
azoxystrobin in the effluent samples from WWTP AI,
spanning a time period from March to end of June. Whilethe concentration of the pharmaceutical is more or lessconstant over the whole sampling period, the concentrationof the pesticide increased over the growing season. This
example demonstrates the different contaminant profiles forcompounds that are continuously introduced, compared tothose that enter the water cycle within a specific period oftime.

AI-4 [Carbamazepine]
237.1022 Area=9.7e06
194.0964 Area=9.2e06
192.0808 Area=1.8e06

AI-5 [Carbamazepine]
237.1022 Area=1.1e07
194.0964 Area=9.9e06
192.0808 Area=2.0e06

AI-6 [Carbamazepine]
237.1022 Area=1.1e07
194.0964 Area=1.0e07
192.0808 Area=2.2e06

AI-7 [Carbamazepine]
237.1022 Area=1.2e.07
194.0964 Area=1.1e07
192.0808 Area=2.5e06

AI-8 [Carbamazepine]
237.1022 Area=1.4e07
194.0964 Area=1.4e07
192.0808 Area=2.9e06

9.8 10.0 10.2

AI-9 [Carbamazepine]
237.1022 Area=1.4e07
194.0964 Area=1.4e07
192.0808 Area=2.8e06

3.2
2.8
2.4
2.0
1.6
1.2
0.8
0.4
0
×10
4.8
4.4
4.0
3.6
3.2
2.8
2.4
2.0
1.6
1.2
0.8
0.4
0

6

9.8 10.0 10.2

9.8 10.0 10.2

9.8 10.0 10.2

9.8 10.0 10.2

9.8 10.0 10.2

9.8 10.0 10.2

9.8 10.0 10.2

9.8 10.0 10.2

AI -1 [Azoxystrobin]404.1241 Area=1.6e07
372.0979 Area=1.7e07
344.1030 Area=5.4e06

AI -2 [Azoxystrobin]404.1241 Area=1.4e07
372.0979 Area=1.6e07
344.1030 Area=4.9e06

AI -3 [Azoxystrobin]404.1241 Area=1.7e07
372.0979 Area=1.8e07
344.1030 Area=5.8e06

AI -4 [Azoxystrobin]404.1241 Area=8.7e07
372.0979 Area=9.5e07
344.1030 Area=3.2e07

AI -5 [Azoxystrobin]404.1241 Area=7.9e07
372.0979 Area=8.5e07
344.1030 Area=2.9e07

AI -6 [Azoxystrobin]404.1241 Area=1.4e08
372.0979 Area=1.5e08344.1030 Area=5.6e07

AI -8 [Azoxystrobin]
AI -7 [Azoxystrobin]404.1241 Area=2.1e08404.1241 Area=1.7e08
372.0979 Area=2.2e08
372.0979 Area=1.8e08
344.1030 Area=9.8e07
344.1030 Area=7.5e07

AI -9 [Azoxystrobin]404.1241 Area=1.8e08
372.0979 Area=2.0e08
344.1030 Area=8.2e07

12.4 12.6 12.8

12.4 12.6 12.8

12.4 12.6 12.8

12.4 12.6 12.8

12.4 12.6 12.8

12.4 12.6 12.8

Figure 3.

12.4 12.6 12.8

12.4 12.6 12.8

12.4 12.6 12.8

Normalized EIC chromatograms of molecular ions and fragments for the antiepileptic drug carbamazepine and the pesticide azoxystrobin over thecourse of the sampling period (March to end of June).

8Extended Suspect screening using thecomprehensive Agilent MassHunter Water
Screening PCDL

Virtually at the same time, using two high energy channels,
compounds are fragmented with two different collisionenergies without precursor selection in very fast sequential
steps, and accurate mass fragment data are recorded
for both channels. When the data is analyzed using theFind‑by‑Formula algorithm, the Water Screening PCDL
provides the precursor formula information, and compound
chromatograms are extracted for all specified ion species.
For putative identifications, chromatograms are automaticallyextracted from the high energy channels for a specified
number of the most abundant fragments from the MS/MS
spectra stored in the PCDL. As an example, Figure 4A
shows the accurate mass library spectrum of valsartan fromthe Water Screening PCDL, compared to the cleaned highenergy spectrum from an effluent sample from the WWTP AI(Figure 4B). The red triangles indicate the fragment ions thathave been selected from the library spectrum for evaluation.

Based on the results from the target screening, the samples
from the WWTPs AI and AZ were selected for an extended
Suspect screening looking for all remaining compounds
included in the Water Screening PCDL. The availabilityof accurate mass MS/MS information is key for theidentification of potential candidates, and is either used in the
All Ions MS/MS workflow for the extraction and alignment of
EICs of the molecular ion and characteristic fragments, or forthe library matching of an acquired accurate mass MS/MS
spectrum against the reference spectra in the PCDL. Bothworkflows are shown in Figure 4 for some of the examples
identified in the WWTP effluents.
All Ions MS/MS screening workflow
In the All Ions MS/MS workflow, accurate mass data iscollected without fragmentation in a low energy channel.

CH 3

A

207.0917 235.0978
100.00 983.48

Library spectrum

291.1479
90.77

H 3C
HO

N

O

N N
HN
N

306.1713
61.28
180.08081.24
60180.0781
12.82

362.22278.29

418.2238
1.62

80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480
Mass-to-charge (m/z)

B
Compound 898 - Valsartan

207.09168

Counts

×10 6
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

CH 3

O

Abundance

120
110
100
90
80
70
60
50
40
30
20
10
0

291.14785
235.09514
180.08078(M+H) +
436.23494

306.16864(M+Na) +458.2167
60

Figure 4.

80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480
Mass-to-charge (m/z)

Accurate mass library spectrum for valsartan at a collision energy of 20 V (A) in comparison to
the acquired high energy spectrum (B) from an effluent sample of the wastewater treatment
plant AI (cleaned spectrum). The red triangles in the library spectrum (A) indicate automaticallyselected ions for the All Ions MS/MS evaluation.

9

showed coelution with the precursor ion. This is also
demonstrated in the coelution plot in Figure 5B. The detailed
compound identification results, including the coelution
scores, are shown in the compound table in Figure 5C.

While the library spectrum is based on a collision energy of
20 V, the cleaned high energy spectrum combines informationfrom both high energy channels acquired with 20 and 40 V.
By overlaying chromatograms for both precursor andfragment ions, and the calculation of a coelution score, theidentity of the angiotensin receptor blocker (ARB) valsartan
was confirmed. The coelution score takes into accountfactors such as abundance, peak shape (symmetry), peakwidth, and retention time. The normalized intensity ratios
are plotted and made available to the user for inspection in a
coelution plot. Figure 5A shows the overlay of the molecularion chromatogram with the fragment chromatograms fromthe high energy channels. All five fragment chromatograms

×10 6
1.6

A

Identification with high confidence is achieved when the
EICs of the molecular ion and at least one or two fragments
show perfect coelution, which is expressed by a coelution
score of > 90 (out of 100), and the mass accuracy for the peak
spectrum for both molecular ions and fragments is better
than 5 ppm. Based on this rule, additional pharmaceuticals
were identified in the effluent of WWTP AI, namely the ARBs
candesartan, irbesartan, and losartan.

11.997
4,684,095

1.4Counts

1.2
1.0
0.8
0.6
0.4
0.2

Fragment ion/precursor ion ratio

0

100
50

11.6

11.7

11.8

11.9

12.0
12.1
Acquisition time (min)

12.2

12.3

12.4

12.5

11.6

11.7

11.8

11.9

12.0
12.1
Acquisition time (min)

12.2

12.3

12.4

12.5

B

10
5
1
0.5
0.1
0.05
0.01

C

Figure 5.

Overlay of precursor and fragment ion traces for valsartan in an effluent sample of the wastewater treatment plant AI (A), coelution
plot (B) and compound identification results including the coelution score (C).

10

Other pharmaceuticals and personal care products that were
not targeted in the previous workflow (target screening
using a reference standard), including the compounds
clarithromycin, fexofenadine, sitagliptin, celiprolol, andphenylbenzimidazole sulfonic acid were also identified.
Moreover, nine further pesticides (napropamid, pyrimethanil,
fenamidone, lenacil, dimethenamid, boscalid, dinoseb,
fludioxonil, and penconazole) as well as perfluorooctanoic
acid (PFOA) and several organophosphates (triethyl
phosphate, tris(2-chloroethyl)phosphate, tributylphosphate,
and triphenylphosphate) were also found. In the AZ
samples, valsartan, candesartan, and irbesartan, as well as
fexofenadine, clarithromycin, venlafaxine, and its metabolite
desmethyl venlafaxine, citalopram, cetirizine, clopidogrel, andritonavir were found. In addition to PFOA, perfluorononanoic
acid was also found. No further pesticides were detected andconfirmed in the effluent of the WWTP AZ.

for compound verification. In cases where there is nolibrary spectrum available, for example, for newly identified
compounds or suspected transformation products, accuratemass MS/MS spectra can be compared to theoreticalfragmentation of a compound in the MassHunter Molecular
Structure Correlator (MSC) software.
Typically, this workflow starts with an All Ions MS/MS
acquisition discovery run, and the tentative identificationof suspected compounds using the Find-by-Formula data
mining algorithm with the fragment confirmation. In cases
when no fragment ions can be identified or if only one
unspecific fragment is observed, the compounds are selectedfrom the MassHunter Qualitative Analysis software, andexported into a target list. In a consecutive run using thesame chromatography, accurate mass MS/MS spectra forthe targeted precursors are acquired. If utmost sensitivity
is required, it might be beneficial to operate the Q-TOF in
TOF mode for the discovery run. Using TOF mode results ina higher number of suspects and, therefore, more precursor
masses for the consecutive targeted MS/MS or auto MS/MS
run. However, in this instance, more of the low abundantcontaminants will be detected. Data analysis for the targeted
MS/MS or auto MS/MS run starts with data mining using
the Find by MS/MS (target or auto) feature finding algorithm
followed by library searching using the MassHunter Water
Screening PCDL.

Suspect screening and verification using targeted MS/MS
The verification of compounds by matching accurate mass
MS/MS spectra against reference spectra from an accuratemass MS/MS library sometimes offers advantages over the
All Ions MS/MS workflow. This is the case if contaminants
are present only at trace concentrations and, thus, signal
intensities of the fragment ions are low, or if interferences
occur between the low mass fragment ions and ions from thematrix. Moreover, accurate mass MS/MS library comparison
after precursor isolation is considered to be the gold standard

11

Figure 6 shows the compound chromatogram and MS1 levelpeak spectrum for the antidiabetic drug metformin found in
the effluent of wastewater treatment plant AZ. Due to thelow mass of the molecular ion and the even lower masses ofthe specific fragments, the compound could not be verified in
the All Ions MS/MS workflow, but was confidently identified
with the targeted MS/MS approach. The predominant ion
species for metformin was [M+H]+, and the measured m/z
was in good agreement with the calculated mass (–0.7 ppm).
The red boxes around the mass signals show the expectedisotope ratio. The measured intensities for the monoisotopic
mass signal, and the [M+1] isotope signal are in very good
agreement with the theoretical pattern. However, there wasan interference observed for the [M+2] signal, and therefore
the target score was only 72.7 (out of 100).

×10 6
1.3

1.539
6,470,857

A
Compound 13: Metformin+ ESI EIC (130.10872, 152.09067)

1.2
1.1
1.0

Counts

0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1.0

1.1

1.2

1.3

1.4

Counts

×10 5
3.2
3.0
2.8
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0

B
Compound 13:
Metformin+ FBF spectrum

1.5
1.6
1.7
Acquisition time (min)Figure 6.

129.0

129.5

1.9

2.0

2.1

2.2

130.10881([C 4H 11N 5]+H) +131.11148([C 4H 11N 5]+H) +
128.5

1.8

130.0

132.10156([C 4H 11N 5]+H) +130.5 131.0 131.5 132.0
Mass-to-charge (m/z)

132.5

133.0

133.5

Compound chromatogram and cleaned peak spectrum obtained by the Find-byFormula algorithm for the antidiabetic drug metformin found in the effluent ofwastewater treatment plant AZ.

12

Counts

CountsCounts

The red diamond in Figure 6 indicates that MS/MS spectra
have been acquired for that particular m/z. MS/MS spectra
were extracted automatically over the peak range, andwere matched against the library spectra contained in the
MassHunter Water Screening PCDL. Figure 7 shows the
MS/MS spectrum for metformin acquired in the wastewater
treatment plant effluent (7A) in comparison to the library
spectrum from the PCDL (7C). A mirror representation of the

×10 2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
×10 2
1.25
1.00
0.75
0.50
0.25
0
-0.25
-0.50
-0.75
-1.00
×10 2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

A

71.06065

60.05570
80.05085

B

71.06065

60.05570
80.05085

C

71.06038

60.05562
80.05087
113.08217
55

Figure 7.

difference spectrum is shown in panel 7B. All major fragment
ions listed in the library spectrum of metformin were foundin the measured spectrum within a narrow mass extraction
window and in a similar ratio as in the reference spectrum fora collision energy of 20 V. Thus, the reverse search against
the accurate mass library resulted in a score of 95.4 out of100, and verified the presence of metformin in the sample.
MS/MS scores were required to be above 60 for verification.

60

65

70

75

80

85 90 95 100 105 110 115 120 125 130
Mass-to-charge (m/z)

Comparison of the measured spectrum of metformin in the WWTP effluent sample(A) with the reference spectrum from the Agilent MassHunter Water Screening
PCDL (C), and a mirror representation of the difference spectrum (B).

13

A

×10 2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

85.05068

68.02406
CountsCountsFigure 8 shows other compound examples that could beverified by using the targeted MS/MS workflow. Melamine
was found in the effluent of AZ with a library score of 85.3.
It has several industrial uses, and is formed as a metaboliteof the pesticide cyromazine. Denatonium was found in theeffluent of AI with a library score of 91.5. It is the most bitter
compound known, and is used as bitterant in personal careproducts to prevent inappropriate ingestion.

60.05566

110.04596

×10 2
1.25
0.75

60.05566

CountsCounts209.12086

135.04591

233.16877
265.17244
209.12086

265.17244

86.09893

0.50
110.04596

0

-0.25

0.25
0

-0.25

-0.50

-0.50

-0.75

-0.75

-1.00

-1.00
Melamine C 3H 6N 685.05087

CountsCounts135.04591

0.75

68.02406

0.25

68.02432
60.05562

50

Figure 8.

233.16877

1.00

0.50

×10 2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

86.09893

×10 2
1.25

85.05068

1.00

B

×10 2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

60

110.04612
70

80
90
100
Mass-to-charge (m/z)

110

×10 2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

120

Denatonium C 21H 29N 2O
86.09643

233.16484

112.07569
60

80 100 120 140 160 180 200 220 240 260 280 300 320
Mass-to-charge (m/z)

Comparison of the measured spectrum of melamine in the WWTP effluent sample AZ (A) and of denatonium in the effluent sample AI (B) with thereference MS/MS spectra from the Agilent MassHunter Water Screening PCDL.

14

Conclusions

References

A method for the screening and (semi-)quantification ofenvironmental contaminants in water samples was developed
and applied to WWTP effluents. This method takes fulladvantage of the low delay volumes of the Agilent 1290
Infinity LC, and its ability to handle high backpressuresin UHPLC separations to increase the chromatographic
resolution. The method benefits from the sensitivity of theAgilent 6550 iFunnel Q-TOF, and from the versatile ionization
capabilities of the Agilent Jet Stream ionization source.

1. P. Gago-Ferrero, et al. Extended Suspect and Non-Target
Strategies to Characterize Emerging Polar Organic
Contaminants in Raw Wastewater with LC-HRMS/MS.
Environ. Sci. & Technol. 49(20), 12333-12341 (2015).
2. Directive 2013/39/EU of the European Parliament andof the Council of 12 August 2013 amending Directives
2000/60/EC and 2008/105/EC as regards priority
substances in the field of water policy.
3. US EPA Method 1694 (2007) Pharmaceuticals andPersonal Care Products in Water, Soil, Sediment,
and Biosolids by HPLC/MS/MS. http://www.epa.
gov/sites/production/files/2015-10/documents/
method_1694_2007.pdf (accessed 14 December 2015).

The Agilent MassHunter Water Screening PCDL is an ideal
complement for the Target and suspect screening workflows
in the Agilent MassHunter Qualitative and Quantitative
software. In Target Screening and (semi-)quantification,
the PCDL is used to define the suite of compounds and to
identify selective qualifier ions. While in suspect screening,
identification with high confidence is achieved by fragment
confirmation or library searching. Applying both workflows to
the WWTP effluent samples revealed the characteristics ofthe treatment technology as well as the catchment area by
the different chemical inventory of trace contaminants.

4. US EPA Contaminant Candidate List (CCL) and Regulatory
Determination: Draft CCL 4 Chemical Contaminants.
http://www.epa.gov/ccl/chemical-contaminants-ccl-4(accessed 14 December 2015).
5. M. J. Gómez, et al. Rapid automated screening,
identification and quantification of organic
micro‑contaminants and their main transformation
products in wastewater and river waters using liquid
chromatography-quadrupole-time-of-flight mass
spectrometry with an accurate-mass database.
J. of Chromatog. A 1217(45), 7038-7054 (2010).

Auto MS/MS acquisition with an inclusion list and a single
collision energy combined with library matching resulted
in similar verification rates as the Agilent All Ions MS/MS
acquisition with fragment coelution. However, it can beseen that the precursor isolation in auto MS/MS acquisition
improved the identification compared to the All Ions MS/MS
workflow in heavy matrix and for low mass contaminants.
Conversely, All Ions MS/MS acquisition is very fast, andallows the differentiation of closely eluting isomers. Anotherimportant feature of the All Ions MS/MS workflow is that thedata can be re-interrogated at a later time for compounds thatwere not in the scope of the analysis during measurement,
allowing for retrospective data analysis for new emerging
contaminants without the need to reacquire data from oldsamples.

6. T. Letzel, et al. LC–MS screening techniques for
wastewater analysis and analytical data handlingstrategies: Sartans and their transformation products as
an example. Chemosphere 137, 198-206 (2015).
7. C. Rodriguez, et al. Indirect Potable Reuse: A Sustainable
Water Supply Alternative. Int. J. Environ. Res. Public
Health 6, 1174-1209 (2009).
8. D-H. D. Yang, et al. Sensitive Screening of Pharmaceuticals
and Personal Care Products (PPCPs) in Water Using an
Agilent 6545 Q-TOF LC/MS System. Agilent Technologies
Application Note, publication number 5991-5954EN (2015).
9. E. L. Schymanski, et al. Non-target screening withhigh‑resolution mass spectrometry: critical review using acollaborative trial on water analysis. Anal. Bioanal. Chem.
407(21), 6237-6255 (2015).

15

For More Information
These data represent typical results. For more information on
our products and services, visit our Web site atwww.agilent.com/chem.

www.agilent.com/chem
Agilent shall not be liable for errors contained herein or for incidental or consequential
damages in connection with the furnishing, performance, or use of this material.
Information, descriptions, and specifications in this publication are subject to change
without notice.
© Agilent Technologies, Inc., 2016
Printed in the USA
March 29, 2016
5991-6627EN

Accurate Mass Analysis of HydraulicFracturing Waters: Identification ofPolyethylene Glycol Surfactants by
LC/Q-TOF-MS
Application Note

Authors

Abstract

E. Michael Thurman and Imma Ferrer

A combination of UHPLC followed by LC/Q-TOF-MS was used to detect suites ofCenter for Environmental Masspolyethylene glycol compounds (PEGs) that occur in flowback water samples fromSpectrometry

hydraulic fracturing. The Kendrick Mass Defect was applied to differentiate the vari-

Department of Environmental

ous adducts within a suite of PEGs. A database of the accurate masses along withEngineering

their retention times by UHPLC has been designed to enable rapid and accurate

University of Colorado

analysis of either groundwater or flowback samples from hydraulic fracturing.

Boulder, COForty PEGs and their various adducts and multiply-charged ions can be identified inUSAless than 2 minutes of computer time.

Introduction

Table 1.

LC and Q-TOF MS Conditions and Analysis Parameters

LC run conditions

Hydraulic fracturing (fracking) extracts oil and gas by forcing
fluids into oil and gas rich shale deposits. The fracking fluids
contain a mixture of proppants (sand), surfactants, biocides,
inorganic salts, and other compounds intended to facilitate
the release of the trapped gas. More than 7,000 wells havebeen drilled in Colorado alone, and reports of groundwatercontamination have occurred most notably in Wyoming, New
York, and Pennsylvania [1]. The first water that returns fromthe fracking process (flowback) has the potential to contaminate nearby aquifers or surface water. Care is taken to recycle
the flowback water, or to dispose of it properly, although the
Safe Drinking Water Act does not apply to hydraulic fracturingsolutions [2]. Thus, there is a demand for tracer compounds
that are indicative of the presence of contamination by
hydraulic fracturing and do not occur in the subsurface [2].

ColumnNebulizer gas

45 psig

In this application note, a combination of ultra high performance liquid chromatography (UHPLC) followed by quadrupole time-of-flight tandem mass spectrometry(Q-TOF-MS/MS) was used to detect a bimodal distribution ofpolyethylene glycols that may be used as indicator compounds for groundwater or surface water impacted by
flowback waters. The Kendrick Mass Defect [2,3] was appliedin a novel application to differentiate the various adducts of asuite of glycols, in particular, the proton, ammonium, andsodium adducts of each of the chains of glycols. The databaseof the accurate masses along with their retention times by
UHPLC is needed to provide a rapid and accurate analysis ofeither groundwater or surface water samples using Agilent
MassHunter Software.

Sheath gas

11 L/min at 350 °C

Capillary voltage

4,000 V

Nozzle voltage

1,000 V

Fragmentor voltage

190 V

Agilent ZORBAX Eclipse XDB-C8, 4.6 × 150 mm, 3.5 µm

Column temperature 25 °C
Injection volume

10 µL

Mobile phaseA) 0.1% formic acid in water v/v
B) Acetonitrile

Linear gradient

10% B for 5 minutes;
10% B to 100% B over 25 minutes

Flow rate

0.6 mL/min

Post run

12 minutes

Q-TOF MS conditions
Ion mode

ESI, positive

Drying gas

10 L/min

Gas temperature

325 °C

Skimmer voltage

45 V

Octopole RF

750 V

Mass range

m/z 50–1,000

Detector rate

2 GHz

Resolving power

30,000 at m/z 1,522

Data analysis
The data were processed with Agilent MassHunter Software.
Accurate mass measurements of each peak from the total ion
chromatograms were obtained by means of an automated calibrant delivery system using a low flow of a calibrating solution (calibrant solution A, Agilent Technologies, Inc.) that contained the internal reference masses purine m/z 121.0509 andHP-921 at m/z 922.0098. The instrument provided a typicalmass resolving power of 30,000 at m/z 1522.

Experimental
Instruments
Separation of the analytes was carried out using an
Agilent 1290 Infinity LC System coupled to an Agilent 6540
Ultra High Definition (UHD) Accurate-Mass Q-TOF LC/MS
system equipped with electrospray Jet Stream Technology.
The instrument conditions are shown in Table 1.

Sample collection
The flowback sample was collected on October 14, 2014 andobtained from Weld County, Colorado, with the help of James
Rosenblum, University of Colorado, Department ofEnvironmental Sustainability.

2

Results and Discussion

Kendrick mass scale
Kendrick used a filtering technique (scaling factor) named
after him to better separate and understand a series of hydrocarbons that differed in mass by a methylene group, CH2. The
Kendrick mass scale has been applied to other series, but only
recently to the polyethylene glycol structures and linear
alkylethoxylates in flowback and produced waters fromhydraulic fracturing by Thurman et al. in 2014 [2].

UHPLC/Q-TOF-MS analysis of hydraulicfracturing waters
The positive ion electrospray total ion current (TIC) chromatogram for the analysis of the flowback water exhibits two
distinct zones, or a bimodal distribution (Figure 1). First is apolar series of peaks at a retention time of 4–12 minutes, andsecond is a less-polar zone that elutes from 12–14 minutes.
The series of peaks in both zones are separated by a nominal
mass of 44 mass units, which suggest a glycol structure consisting of CH2-CH2-O (Table 2). Furthermore, the accuratemass data in Table 2 show that the differences for the firstseven peaks are 44.0266, on the average. A similar result wasalso observed for the second region between 12–14 minutes
in Figure 1 (data not shown). There is an apparent repeatable
relationship among the chromatographic peaks, which makes
it is possible to apply the Kendrick mass scale [3].

The application of the Kendrick mass scale first involves thedetermination of the Kendrick mass scaling factor, which isdetermined as the ratio of the nominal mass of the repeating
glycol unit, (CH2CH2-O), divided by the exact calculated massof the same glycol unit. This is equal to 44/44.026214748,
which gives a scaling factor of 0.999404559. When this scaling factor is multiplied by the measured accurate mass, theresulting mass is called the Kendrick mass [2]. For example,
Table 2 shows the Kendrick masses for some of thechromatographic peaks in Figure 1.

Counts

×107
4.8
4.2
3.6
3.0
2.4
1.8
1.2
0.6
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Acquisition time (min)Figure 1.

UHPLC/Q-TOF-MS chromatogram of a flowback sample from a recently hydraulic fractured well, which shows a bimodal distribution of polymers that differ by 44.026 massunits, a hydrophilic zone at 4–12 minutes and a second less-polar zone at 12–14 minutes.

Table 2.

Kendrick Mass for a Suite of Polyethylene Glycols Found in
Flowback Water with an Average Mass Difference of 44.0266*

Retention time(min)

Ion (m/z) measured
accurate mass

Kendrickmass

Ion adduct

3.5

173.0776

172.975

Na + PEG-EO-3

4.2

217.1048

216.975

Na + PEG-EO-4

5.4

261.1309

260.975

Na + PEG-EO-5

7.3

305.1586

304.975

Na + PEG-EO-6

9.5

349.1830

348.975

Na + PEG-EO-7

10.2

393.2095

392.975

Na + PEG-EO-8

10.7

437.2373

436.977

Na + PEG-EO-9

10.7

432.2830

432.026

NH4 + PEG-EO-9

11.0

476.3067

476.023

NH4 + PEG-EO-10

11.3

520.3333

520.023

NH4 + PEG-EO-11*JR-5 sample from Weld County, Colorado, based on a scaling factor of 0.999404559.

3

The concept of the Kendrick mass defect can then be applied,which is that if two compounds have the same repeatingchemical structure, that is, (CH2CH2-O), then there will be an
addition of the exact mass of CH2-CH2-O to each new compound in a chromatogram. Thus, when the scaling factor ismultiplied by the measured masses, all the ions that differ by
the CH2-CH2-O group will have exactly the same mass defect(within the error of accurate mass measurement, which istypically \0.001 mass units). These results are shown in
Table 2.

suite of 9 ions (Table1). The fact that all of the Kendrick massdefects for each type of ion adduct are nearly identical meansthat each of the compounds increases by one ethylene glycol
unit, or an accurate mass of 44.0262. Thus, it is only necessary to obtain the correct formula and structure for one of theglycols, and then all others can be calculated as either one
unit longer or shorter depending on the gain or loss of the44.0262 mass unit.
Using the measured mass of 305.1586 (a sodium adduct inTable 2), which also shows a proton adduct at m/z 283.1753
in Figure 2, a formula can be determined using MassHunter
Software, as shown in Figure 2. The proton adduct at
283.1753 is first highlighted, followed by using the Generate
Formula option to determine the best formula, shown in
Figure 2 as C12H26O7, which is the neutral molecular formula.

For example, Table 2 shows that the measured accuratemasses for ions at retention times of 3.5 to 10.7 minutes all
had different mass defects ranging from 0.0776 to 0.3333.
However, after multiplying by the appropriate Kendrick massscaling factor for the glycol unit (CH2CH2-O) of 0.999404559,
only two mass defects, 0.975 and 0.023, were found for this

×106
7.0
6.5

305.1586

6.0
5.5
5.0

Counts

4.5
4.0
3.5
3.0

283.1753(C12H26O7)+H+

2.5
2.0
1.5
1.0
0.5
0
260

Figure 2.

271.1151
270
280

321.1286
290

300

310
320
330
340
Mass-to-charge (m/z)

347.1675350

360

371.1311
370
380

Q-TOF-MS scan from 7.060 to 7.409 minutes, showing the proton adduct at m/z 283.1753
along with the formula generated in Agilent MassHunter Software. The sodium adduct of
the ion is also shown at m/z 305.1586.

4

Analysis by MS/MS of the m/z 283.1753 can determine if thespectrum will match a polyethylene glycol structure, as
shown in Figure 3. The mass spectrum consists of a series of
44.026 mass unit losses that translate into a simple PEG
structure [4]. The polyethylene glycol unit is equal to
HO-(CH2CH2O)n-H, which means that the chain length of theunit must be n = 6, since this length provides the correct formula of C12H26O7. Table 2 can then be used to assign PEG-7
and PEG-8 to the higher mass ions, as well as PEGs -3, -4,
and -5 to the lower mass ions, since all of the compoundswith the same Kendrick mass differ by one glycol unit.

Figure 4 shows the mass spectrum for PEG-9 at a retention
time of 10.6 minutes with three major ions at m/z 415.2539,
432.2830, and 437.2378. The mass differences among thesethree ions are 17 and 22, which indicate that the proton
adduct is at 415.2539. What is important to note about thePEG-9 is that the major adduct ion for PEG-9 is no longer thesodium adduct, but rather the ammonium adduct (compare
Figures 2 and 4). The mobile phase does not contain ammonium; thus, the ammonium adduct is formed from trace levelsof ammonium present in the mobile phase and sample [2].
This shift to the ammonium adduct at PEG-9 has been attributed to a cage-like structure that surrounds the ammonium
ion more readily (that is, energetically) and favors the ammonium ion over the sodium ion adduct [2]. The same is true forammonium adducts of PEG-10 and PEG-11.

Counts

×104
4.0
3.8
3.6
3.4
3.2
3.0
2.8
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0

89.0600
44.026 mass units

133.0855

283.1743

177.1117151.0960

195.1222

236.1480

73.0637

114.0561

60

100 120 140 160 180 200 220 240 260 280 300 320 240 360 380 400
Mass-to-charge (m/z)

80

Figure 3.

MS/MS spectrum of the m/z 283.1743 proton adduct from Figure 2.

×106
8.5
8.0
7.5
7.0
6.5
6.0
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0

*432.2830

Counts437.2378

415.2539

427.1576
405

Figure 4.

410

415

420

425

430

446.2600

453.2099

435 440 445 450
Mass-to-charge (m/z)

455

461.2002
460

476.2874

465

470

475

480

The mass spectrum at 10.6 minutes for PEG-9, with the major ion being the ammonium
adduct at m/z 432.2830.

5

An accurate mass database of PEG compounds
After the application of this Kendrick mass scale, it was possible to identify proton, sodium, and ammonium adducts ofthe various PEGs shown in Figure 1. These enabled the construction of an accurate mass database of these PEG ionsand adducts. It also allowed subsequent use of the
MassHunter Software to find the > 40 different PEGs shownin Figure 1, as well as their various ion adducts, in less than
2 minutes of computer time. Identification of these PEGs may
enable their use as indicators of contamination of aquifers or
surface water by hydraulic fracturing.

Conclusions
The Agilent 1290 Infinity LC System and Agilent 6540
LC/Q-TOF-MS enabled the UHPLC/MS/MS characterizationof two sets of representative polyethylene compounds fromhydraulic fracturing flowback samples. Applying the Kendrick
mass scale to the accurate masses of these PEGs and otherethoxylated surfactants [2] facilitates their identification
using Agilent MassHunter Software, and the construction ofa database of such compounds present in flowback samples.
This database can, in turn, enable the use of thesecompounds as unique tracers of hydraulic fracturing.

References
1. D. M. Kargbo, R. G. Wilhelm, D. J.Campbell. “Natural gas
plays in the Marcellus Shale: Challenges and potential
opportunities” Environ. Sci. Technol. 44, 5679-5684(2010).
2. E. M. Thurman, I. Ferrer, J. Blotevogel, T. Borch.
“Analysis of hydraulic fracturing flowback and produced
waters using accurate mass: identification of ethoxylated
surfactants” Anal. Chem. 86, 9653-9661 (2014).
3. E. Kendrick. “A mass scale based on CH2 = 14.0000 for
high resolution mass spectrometry of organic compounds” Anal. Chem. 35, 2146-2154 (1963).
4. I. Ferrer, E. T. Furlong, E. M. Thurman, in Liquid
Chromatography/Mass Spectrometry, MS/MS andTime-of-Flight MS: Analysis of Emerging Contaminants,
Ferrer, I. Thurman, E. M, Eds.; ACS Symposium Series
850; American Chemical Society: Washington, D.C.
pp 376-393.

Acknowledgements
The authors thank James Rosenblum of the University ofColorado for sample collection and for sharing the flowback
sample for this application note.

www.agilent.com/chem
Agilent shall not be liable for errors contained herein or for incidental or consequential
damages in connection with the furnishing, performance, or use of this material.
Information, descriptions, and specifications in this publication are subject to change
without notice.

© Agilent Technologies, Inc., 2015
Printed in the USA
February 18, 2015
5991-5473EN

Using a Chlorine Filter for Accurate-Mass
Data Analysis of Environmental Samples

Application Note
Environmental

Authors

Abstract

Imma Ferrer and E. Michael Thurman

A chlorine mass-filter is used to screen both LC/TOF-MS and LC/QTOF-MS data files

Center for Environmental Massin order to discover compounds that contain chlorine. The chlorine filter uses Mass

Spectrometry

Hunter software to generate formulae of chlorine containing compounds. Examples

University of Colorado

are shown in the analysis of an environmental water sample (the South Platte River

Boulder, Colorado

after wastewater discharge) for pharmaceuticals. The chlorine filter is a useful dataUSA

analysis tool for complex sample analysis in the field of environmental chemistry.

Introduction
Data files generated by LC/TOF-MS and LC/QTOF-MS contain literally thousands ofindividual ions that are difficult to evaluate by manual techniques. It is important tohave software that makes data analysis rapid and effective. One such data analysis
tool that we report in this application note is the chlorine mass filter. Chlorine appears
in many pesticides and pharmaceutical products that are important to environmental
analysis. Because chlorine contains two isotopes, Cl35 and Cl37, there is a distinctive
A+2 isotope pattern that is generated by a single chlorine atom in a molecule.
Furthermore, there is a isotopic mass defect that occurs with chlorine-37 that makes
the identification of chlorine in a molecule relatively easy [1]. More than one chlorine
atom in a molecule generates an A+2 and A+4 isotopic pattern, which is characteristic and commonly shown in all mass spectrometry books as a key to compound
identification of chlorinated compounds [2].
In this application note, we have automated the MassHunter software to generate formulae that contain chlorine from a data file of a water sample from surface water thatis contaminated with wastewater. Furthermore, the automated report includes a database search using either a forensic or pesticide database for compound identification.

spacing of 0.0025 plus 7 ppm, isotope model is common
organic molecules, and limit assigned charge state to a maximum of one. The Mass Filter is blank and the Mass Defect isblank. The Results is to extract the EIC, highlight all compounds, and delete previous results. These settings should
result in a clean run of the program at this point, that is, theMolecular Feature.

At this point the user needs only to check the data report for
quality control and quality assurance purposes. The chlorine
mass filter will work on any .d data file from environmental
samples, such as food for pesticides or water samples forpharmaceuticals.

Experimental Conditions

Step 2. Run the Find by Molecular Feature program by clicking
the arrow shown in Figure 1. This program identifies all of theions in the .d file and groups ions together that are related,such as isotopic clusters and sodium adducts. It does not groupfragment ions, but considers them as different compounds.

Any of the Agilent accurate mass analysis instruments, theModel 6200 or 6500 series, may be used to generate data files
for this analysis procedure. We applied the 6220 to generate
data files of pharmaceuticals in surface water, which is fromthe South Platte River and is affected by the Denver
Metropolitan area and wastewater from Denver. The data files,
as .d files, are ready for software analysis using the
MassHunter software package with Qualitative Analysis. The
same Qualitative Analysis software is available on all of theaccurate mass instruments, the Model 6200 or 6500 series.

Results and Discussion
Applying the Chlorine Mass Filter
The chlorine filter is applied by an eight-step procedure thatbegins with first opening the Qualitative Analysis software
package and opening the .d file of interest.

Figure 1.

Step 1. Open the .d file of interest with the Qualitative Analysis
software package, the green icon. Go to the Find Compounds
Menu (see Figure 1) and open the Find by Molecular Featuretab. Figure 1 shows the blue arrow that activates this program.
The tab open is called compound filters and is set for a relative
height of 1.5% and absolute height of 10,000 counts. To obtaindetection of even lower abundance compounds, the relative
height might also be left unchecked. However, these settings
may need to be adjusted for the noise levels of both individual
instruments and data sets. These three settings allow for a
filter of a complicated sample and take full advantage of thesoftware’s ability to find small peaks that contain chlorine.

Find by Molecular Feature.

Step 3. Open the Identify Compounds tab (Figure 2) and click
the Generate Formulas tab. This opens the window shown in
Figure 2, and here, one specifies two chlorine atoms and at
least one chlorine atom in the table (see Figure 2 with arrows).
Note that this example filters the compounds for only 1–2 chlorines but one could select as many chlorines as desired. Limits
tab and Charge State tabs are shown in Figure 3 with a settingthat will give good results. Copy these values.

The other settings needed are ion species at H+ and Na+ forthe two positive ion species. In negative ion, the setting wouldbe H-. Extraction is for small molecules (chromatographic) anduse peaks with height greater than 1000 counts. Again, thissetting may need to be adjusted for individual instrument background and sample set noise, Charge State is set for peakFigure 2.

2

Identify Compounds and Generate Formulas.

Figure 3.

Other tab settings.

Step 4. Run the Generate Formulas program with the forced
chlorine atoms. This generates formula for all of the featuresthat were identified in Step 2 and will include only formulaethat contain 1 to 2 chlorines.
Step 5. Open the Search Database pane and open the tabcalled Search Criteria. Select Molecular Formula as shown in
Figure 4. This will force the search to report only those compounds that match the found formulae (and will only contain
1 to 2 chlorines). Next select the Database. Here specify the
Forensic or Pesticide Database (Figure 5). The tab settings are:+ Ions set at +H, -Ions set at –H, Search Results set at Blank,
Peak Limits set at 5, and Search Criteria set at Mass and5 ppm. These settings will work well to identify compounds.
The Forensic database contains over 7,500 compounds including pharmaceuticals and pesticides. The chlorinated compounds are grouped under a column labeled MFG Formula ormolecular formula generator.

Figure 5.

Search Database program using forensics database of 7500
compounds.

Step 6. Run the Search Database Program. This program now
goes in and checks all of the formulas that were generated in
Step 4 and assigns a name to the compound if it appears in the
database. If the formula is not in the database, the Name is left
blank in the final report. In this case, the best fit formula isprinted in the column labeled Molecular Formula Generator for
all chlorinated compounds.
Step 7. Print the Report. The report contains the following
columns: Compound label, Retention Time, Mass of Neutralcompound, Name (if available in the database), Molecular
Formula Generator (MFG) elemental formula, MFG difference inmass accuracy, Database Formula (DB), and DB difference inmass accuracy. The report for the river sample is shown in
Figure 6.

Figure 4.

Select search criteria by Molecular formula.

3

Figure 6.

Printout of Report for database search of forensics to determine for the first time an antidepressant in riverwater [3].

Step 8. QA/QC Manual Report. Finally, it is necessary for theoperator to manually check the assigned chlorinated compounds. This is done by manually calling up the ion at the correct retention time and checking the isotopic signature of theion. Figure 7 shows an example of this for lamotrigine found in
the South Platte River that received wastewater from a nearby
effluent. This is an antidepressant drug that was detected in
the sample at a retention time of 13.4 minutes [3]. The measured mass of the ion was m/z 256.0150 and shows an isotopic
signature for both an A+2 and an A+4 isotope at masses ofm/z 258.0121 and m/z 260.0092. It is important to note the relative isotopic mass defect of both the A+2 and the A+4 isotope
for this compound. The relative mass defects are -0.0029
and -0.0029 for both peaks. These values are quite close to thetheoretical value of -0.0030 and are excellent evidence for thepresence of two chlorine atoms in the molecule [1]. This step isthe critical step of the chlorine filter and is a manual step at
this time. Because the molecular formula generator looks atmass only, it is possible for some formulae to be generated that

Figure 7.

4Mass spectrum for lamotrigine.

do not fit the isotopic signature. In this example from the South
Platte River, we had approximately 100 possible chlorinatedpeaks of which 20 were identified to contain chlorine based on
the relative mass defect and the isotopic signature.

Cl

Of these 20 different ions, there were 15 different components
and the remaining five ions were fragment ions based on theretention time matching of the ions. For example, Figure 8
shows the structure of cetirizine, which was matched by theforensics database at 18.3 minutes with a protonated molecule
at m/z 389.1626 and a fragment ion at the same retention timewith a m/z 201.0466. The combination of mass accuracy, database matching, and identifying a fragment ion shows the powerof using the chlorine mass-filter to find and identify trace chlorinated substituents in water samples impacted by wastewater.
The application shown here of the chlorine mass-filter will work
equally well for food samples contaminated with pesticides orother similar environmental samples.

Cl

+

+

HN
N
C 21H26CIN2O3 +Exact Mass: 389.1626

C13H10CI+
Exact Mass: 201.0466

O
HOO
Figure 8.

Conclusions
Using accurate mass LC/MS with the Agilent 6200 or 6500
series TOF or Q-TOF and MassHunter Qualitative analysis version 5.0, a chlorine filter is described that effectively mines
single MS data for all compounds that contain 1 to 2 chlorines.
The process can be modified to contain any specified numberof halogens or for that matter any specific elements, for example a fluorine filter or iron. The process can be automated using
Worklist Automation and then manually QC’ed as described.

References
1. E.M. Thurman, I. Ferrer, 2010, The isotopic mass defect: A
tool for limiting molecular formula by accurate mass:
Analytical and Bioanalytical Chemistry, v. 397: 2807-2816.
2. R.M. Smith, 2004, Understanding Mass Spectra, John
Wiley & Sons, Inc., New York, 290p.
3. I. Ferrer, E.M. Thurman, 2010, Identification of a new antidepressant and its glucuronide metabolite in water samples using liquid chromatography/quadrupole time-offlight mass spectrometry: Analytical Chemistry,v. 82: 8161-8168.

For More Information
For more information on our products and services, visit our
Web site at www.agilent.com/chem.

5

Cetirizine example showing the two ions that were found at thesame retention time of 18.3 minutes.

www.agilent.com/chem
Agilent shall not be liable for errors contained herein or for incidental or consequential
damages in connection with the furnishing, performance, or use of this material.
Information, descriptions, and specifications in this publication are subject to change
without notice.

© Agilent Technologies, Inc., 2011
Printed in the USA
December 5, 20115990-9431EN

Identification of Unknown
Microcystins in Alberta Lake Water

Application Note
Environmental

Authors

Abstract

Ralph Hindle

Detection, characterization, and tentative identification of very low levels of

Vogon Laboratory Services Ltd.

unknown microcystins in lake water are possible in the absence of analytical stan-

Cochrane, Alberta

dards using a combination of triple quadrupole LC/MS and LC/Q-TOF analysis and a

CanadaPersonal Compound Database (PCD) compiled from the Word Health Organization

Xu Zhang and David Kinniburgh
Alberta Centre for Toxicology
Alberta Health & Wellness
University of Calgary
Calgary, Alberta
Canada(WHO) list of microcystins.

Introduction

Glu6
HO

The occurrence of cyanobacterial toxins in Canadian fresh
waters is a serious environmental and public health concern[1]. Microcystins (MCs) are a class of common cyanobacterial
toxins present in Canadian lakes, and they are potent
inhibitors of eukaryotic protein phosphatases [2].
Microcystins are also powerful hepatotoxins, and may promote tumor development in mammals, presenting a serious
threat to livestock and human drinking water sources. Thesecyanobacterial toxins have been detected in every province inCanada, often at levels above maximum guidelines for recreational water quality [2].

Adda5
O

O

C

N

HN

CH3

H3C

Mdha7
O

CH3

O

O

CH3

NH

CH2
H3C

O
R2

NH

R4

CH3

NH Ala1O

C
HO

MeAsp3O

General microcystin structure

The MCs are cyclic peptides containing seven amino acids.
They have the general structure shown in Figure 1. Table 1
gives the chemical structures for eight microcystins surveyed
in Alberta lakes. The most frequently reported MC is LR,
containing leucine and arginine at positions R2 and R4,
respectively.
The Alberta Centre for Toxicology (ACFT) conducted a comprehensive study of microcystins in Alberta lakes, and it continues to monitor fresh water sources using liquid chromatography and triple quadrupole mass spectrometry. While thismethod tests for eight microcystins, a nontargeted compound
was also detected in a sample with the same transition qualifier ion as MC YR (m/z 1045), but it had an incorrect retention
time (RT) and qualifier ion ratio.

Position

Abbreviation

Amino acid

R1

Ala1

AlanineR2

Leu2 (L)

LeucineR3

MeAsp3

Methylaspartic acidR4

Arg4 (R)

Arginine

R5

Adda5

3-amino-9-methoxy-2,6,6-trimethyl10-phenyldeca-4(E),6(E)-dienoic acid

R6

Glu6

Glutamic acid

R7

Mdha7

N-methyldehydroalanine

Figure 1. General microcystin (MC) structure.

Table 1. Eight Microcystins Surveyed in Alberta Lakes*

This application note describes two methods developed
through a collaboration between Alberta Centre for
Toxicology (ACFT) and Vogon Laboratory Services to provide
sensitive detection of a wide range of microcystins andestablish the identity of this newly observed compound. A
confirmation method was first developed using an Agilent
1290 Infinity LC System and an Agilent 6460 Triple
Quadrupole LC/MS, with a different retention time patternfrom the reference ACFT method. Accurate mass determination on an Agilent 6540 Q-TOF LC/MS system was then used
to provide tentative identification of the unknown peak as
desmethylated microcystin HtyR.

Microcystin

R2

R4

FormulaLR

LeucineArginine

C49H74N10O12 994.5488

Desmethyl LR Leucine

Arginine

C48H72N10O12 980.5331

RR

Arginine

Arginine

C49H75N13O12 1037.5658

YR

Tyrosine

Arginine

C52H72N10O13 1044.5280

LA

Leucine

Alanine

C46H67N7O12

909.4848

LW

LeucinePhenylalanine C54H72N8O121024.5270

LF

LeucineTryptophan

C52H71N7O12

985.5161

HtyR

Homotyrosine Arginine*See Figure 1 for the general microcystin structure

All microcystins were detected at 0.1 ng/mL, which is well
below the 2007 Canadian Drinking Water Guideline of 1.5 µg/L.
Using a Personal Compound Database (PCD) of 52 compounds
created in the Agilent MassHunter software suite from theWorld Health Organization (WHO) list of microcystins, the
Q-TOF data enabled the tentative identification of an additional
seven microcystins.

2

Neutral mass

C53H74N10O13 1058.5437

Experimental

Analysis parameters

Instruments

The 6460 Triple Quadrupole LC/MS multiple reaction
monitoring (MRM) analysis parameters are shown in Table 3.

The confirmation method was developed on an Agilent 1290
Infinity LC System equipped with an Agilent G4226A
Autosampler and coupled to an Agilent 6460 Triple Quadrupole
LC/MS System. The instrument conditions are listed in Table 2.

Table 3. MRM Analysis Parametersa for the Target Compounds Using a
Triple Quadrupole LC/MS

The method for tentative identification of unknown microcystins was developed on a 1290 Infinity LC system equippedwith a G4226A Autosampler and coupled to an Agilent 6540B
Q-TOF LC/MS System with Jet Stream electrospray source.
The instrument conditions are listed in Table 2.

Microcystin

Precursorb
(m/z)

LR

995.6

Desmethyl-LR

981.5

Table 2. LC and Triple Quadrupole MS Run Conditions

RR

520.0

YR

1045.5LA

910.5

LC conditions
ColumnColumn temperature
Injection volume
Mobile phase
Autosampler
temperature

Agilent Poroshell SB-C18, 3.0 × 100 mm, 2.7 µm(p/n 685975-306)
50 °C
20 µL
A) 1 mM ammonium fluoride in water (HPLC grade)B) 20% isopropanol in acetonitrile (LC/MS grade)

0.6 mL/min

Gradient

Time (min)
0
3.0
5.0
6.0
7 minutes
2 minutes

LW
%B
20
30
50
100

350 °C

Drying gas flow

12 L/min

Nebulizer pressure

40 psig
11 L/minCapillary voltage

4,000 V

Nozzle voltage

1,000 V

EMV

400 V
Targeted MS/MS

Acquisition

Profile and centroid; 2 GHz

Range

100–1,700 amu

Acquisition rate (MS)

3 scans/s

1059.5

80

135.2

80

213.2

80

135.2

30

213.2

40

135.2

80

213.2

70

135.2

70

213.2

70

135.2

80

213.2

70

135.2

80

213.2

60

135.2

70

213.2

50

135.2

80

213.2

70

Water samples (10 mL) were taken through threefreeze/thaw cycles, then sonicated for 5 minutes, followed byfiltration through 0.2-µm cellulose filters directly into
autosampler vials.

Acquisition rate (MS/MS) 1 scan/s
Reference masses

HtyR

213.2

Sample preparation

Q-TOF MS conditions
Mode

986.5

80

b All precursors are singly charged, except RR which is doubly charged.

Sheath gas temperature 400 °C
Sheath gas flow

LF

135.2

transitions.

ESI with Agilent Jet Stream Technology

Drying gas temperature

1025.5Collision energy(V)a The fragmentor and cell acceleration voltages were 150 V and 2 V, respectively, for all

Triple quadrupole MS conditions
Ionization mode

1002.55 °C

Flow rate

Stop
Post time

LY

Product ion(m/z)

121.0509 and 922.0098

3

Confirmation method

Results and Discussion

The triple quadrupole LC/MS confirmation method was optimized to provide a minimum detection level at approximately
10% of the Canadian Drinking Water Guidelines (CDWG),
which is 1.5 µg/L, based on MC LR. The lowest level calibrators used for the method were 0.1 µg/L for MCs YR, LR, andRR, and 0.2 µg/L for all others. Figure 2 illustrates the complete separation achieved for nine MCs with the high performance liquid chromatography (HPLC) chromatographic
method at both the lowest calibrator levels and calibrator
levels near the CDWG.

Identifying an unknown microcystin
One sample analyzed using the ACFT method revealed a compound with the same transition and qualifier ion as
YR (m/z 1045), but at the wrong retention time (RT) and qualifier ion ratio. It was tentatively identified as desmethyl-HtyR,
based on the loss of 14 amu from HtyR (m/z 1059).
To confirm the identification of this unknown MC, a triple
quadrupole LC/MS confirmation method was developed,
which provided a different retention time pattern from the reference ACFT method. Additional microcystin analytes wereadded to the method to help characterize compounds. Finally,
samples were also analyzed using an LC/Q-TOF method toconfirm the identity of the unknown compound.

A

Counts

1.0

×10 2

2.28

0.5

×10 2

Counts

2.38

6
4

×10 2

CountsCountsCountsCounts1

Counts

×10 2

2.61

2.78

LA

RR

2.90

2.97

Counts

0.5
4.51

2
0

LW

3.45

4.36

×10 2

4.50
5.0 LF
2.50
2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6
Acquisition time (min)Counts

×10 1

4 LY
2
0

×10 2

4.37

1.0

Counts

5
0

2.33

×10 2

3.50

6
5
4

5
0

×10 3

2.98

×10 1

4 dm-LR
2
0

×10 2

2.97

×10 2

Counts

×10 2

2.82

5.0
2.5
0

Counts

1.0 LR
0.5
0

6
4

×10 2

HtyR

CountsCountsCounts

×10 1

2
0

2.24

×10 3

2.70

1.0
0.5

CountsCounts

×10 1

B

5.0 YR
2.5
0Counts

×10 2

6
4
2.0

2.2

2.4

2.6

2.8

3.0 3.2 3.4 3.6 3.8
Acquisition time (min)

4.0

4.2

4.4

4.6

Lowest level calibrator

Calibrator near DW guidelines

0.1 µg/L for YR, LR, and RR
0.2 µg/L for all others

0.9 µg/L for YR, LR, and RR
1.9 µg/L for all others

Figure 2. Example total ions chromatograms (TICs) for nine microcystins at concentrations approximately 10% (A) and 100% (B) of the CDWG of 2007.

4

Quantitation calibration coefficients (R2) were excellent, ranging from 0.9978 to 0.9995 for the six MCs using a minimum
calibrator level of 0.2 µg/L and a maximum calibrator level of
50 µg/L (Table 4). The R2 values ranged from 0.9992 to 0.9997
for the three MCs using a minimum calibrator level of
0.1 µg/L and a maximum calibrator level of 25 µg/L. A representative calibration curve for MC LR is shown in Figure 3,
illustrating excellent linearity even at extremely low concentrations. Accuracy ranged from 84% to 121% for the six MCs
with a minimum calibrator level of 0.2 µg/L, and 110% to119% for the three MCs with a minimum calibrator level of
0.1 µg/L (Table 4).

Analyzing the unknown
Using the confirmation method to analyze the unknown MCfrom an Alberta lake water sample showed a peak with thesame transition and qualifier ions as YR, but with an RT of
1.91 minutes, rather than 2.24 minutes. The qualifier ion ratio(m/z 213.2/135.2) was also too low to be YR (2% versus24%). The low abundance of the m/z 213.2 peak indicatespossible desmethylation of the N-methyldehydroalanine(Mdha) moiety in the YR structure. Unfortunately, desmethyl
HtyR is not commercially available. Therefore, Q-TOF for
accurate mass analysis was used to further confirm thedesmethylation hypothesis.

Table 4. Calibration Coefficients and Accuracy of Recovery
R2

Accuracya (%)

Hty R

0.999595

Desmethyl-LR

0.999384LA0.9988121

LY

0.999892

LW

0.9989118

LF

0.9978119

YR

0.9993118

LR

0.9997110

RR

0.9992

119

Calibration range MC

0.2 to 50 µg/L

0.1 to 25 µg/L

a Quantitative accuracy is listed for calibrator 1 (0.1 or 0.2 µg/L)

Expanded range from 0.10 to 0.92 µg/L
×103
4
3.5
3
2.5
2
1.5
1
0.5
0

×105

Responses

Responses

1.2
1
0.8
0.6
0.4

0

0.2

0.1

0.2

0.3 0.4 0.5 0.6 0.7
Concentration (ng/mL)

0.8

0.9

1

0
0

2

4

6

8

10 12 14 16 18
Concentration (ng/mL)

20

22

24

26

Figure 3. A calibration curve for MC LR using three-fold serial dilutions from 0.1 to 25 µg/L, including the expanded range from 0.10 to 0.92 µg/L, to illustrate
excellent linearity.

5

LC/Q-TOF analysis

desmethylation of HtyR. Examination of the transition ionsreveals that both the unknown and YR contain the two m/z 135
nominal mass ions characteristic for microcystins (Figure 4).
The actual masses of the two ions are 135.0804 and 135.1168,
formed by different fragmentation patterns of the Adda group.
Both ions are well separated in both microcystins at mass
resolution 14,000.

The accurate mass determined for the precursor ion for the
unknown MC using liquid chromatography/Q-TOF MS(LC/Q-TOF) was 1059.5500, which matches the actual mass fordesmethylated HtyR with a mass error of 5.0 ppm. This further
supports the hypothesis that the unknown is formed by the

HO

×10 3

135.0798
14,079CH3

O

136.0761
11,504

CH3

H3C

O

O

CH3

NH

O

137.0783
14,237OCCounts

O

CH3

H3C

134.8

135

136.1185
136.0821 14,569

135.1168 = C 10H15 +135.2 135.4 135.6 135.8 136 136.2 136.4 136.6 136.8
Mass-to-charge (m/z)

137137.2 137.4

O

CH3

O

O

CH3

NH
CH3

O
NH

CH2
H3C

O

C
HO

O
X

NH

Z

O

The two Adda ions formed from all
microcystins during analysis MS.

Figure 4. The two ions derived from the Adda group and characteristic of all microcystins are present and well separated in both YR and the unknown
microcystin, using LC/Q-TOF.

6

O
X

N

HN

Unknown microcystin

135.1157
14,372C
HO

135.0804 = C 9H11O +HO

135.0795
13,998

NH
CH2
H3C
NH

Z

CH3

136.1188
13,258135.1158
14,383

O

N

HN

YR

Counts

1.4
1.2
1.0
0.8
0.6
0.4
0.2
0
×10 3
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0

O

C

A previous structural LC/MS/MS characterization of microcystins had identified eight major ions designated a-h [3]. Twoof these, ions f and h, can be used to confirm the identity of
the unknown MC. Ion f contains the R7 and R2 moieties(Figure 1), which should be dm-Mdha (Dha) and Hty, respectively for the microcystin dm-HtyR. Ion h contains the R2
moiety, as well as a second possible desmethylation site,
MeAsp3. Depending on the identities of R7 and R2 and thepresence or absence of desmethylation at position R3, theaccurate masses of these two ions will vary and be diagnosticof the microcystin structure.

However, the m/z 213 ion formed from cleavage of the Glu +Mdha groups from the microcystins is present as expected in
MC YR, but present at only trace levels in the unknown MC(Figure 5). This indicates that the unknown MC is not YR.
Desmethylation of the Glu + Mdha group would be expected to
produce an ion with an accurate mass of 199.0713 (Figure 6).
This ion is observed in the spectra from the unknown MC, but
not in the spectra of MC YR. These results also support the
hypothesis that the unknown MC is dm-HtyR.

×10 2
6

Counts5

Glu 6 + Mdha 7 = C 9H 13N 2O 4 +m/z = 213.0870

213.0862

YR

4

HO

3

CH3

1

O

214.0899

213.1342

0
×10 2
6
5

CH3

H3C

O

CH3

O
R2

NH

R4
O

Unknown microcystin

NH
CH2
H3C

O

NH
CH3

O

N

HN

2

Counts

O

C

C
HO

O

4
3
2
213.1322

1
0
212

212.2 212.4 212.6 212.8 213.0 213.2 213.4 213.6 213.8
Mass-to-charge (m/z)

214214.2 214.4

Figure 5. Q-TOF spectra of YR and the unknown microcystin in the m/z range from 212 to 214.4. The m/z 213 ion characteristic of the Glu + Mdha group ispresent in microcystin YR but at low abundance in the unknown MC.
×10 2
2.5

YR
200.1130

Counts

2.0
1.5
1.0

201.0982

0.5
0
×10 2
2.5

200.1129

Unknown microcystin

Counts

2.0
1.5

0.5
0

201.0969

199.0695

1.0

Desmethylation of Glu 6 + Mdha
C 8H11N 2O 4 + fragment ion from
[Glu 6 + Mdha7 − CH 2] will haveaccurate mass 199.0713

198.1221
198

198.5

199

199.5

200.5
201
200
Mass-to-charge (m/z)

201.5

202

202.5

Figure 6. Q-TOF spectra of YR and the unknown microcystin in the m/z range from 198 to 202. The
m/z 199 peak is present in the unknown microcystin, indicating desmethylation of the
Glu + Mdha group. This peak is not present in YR.

7

Analysis by Q-TOF of ion f in the unknown microcystin
revealed accurate masses that confirmed the presence of Dha
at position 7 and Hty at position 2, consistent with the identification of the unknown as dm-HtyR. Analysis of the HtyR
standard also gave the correct accurate mass for ion f(Figure 7).

×10 3
1.0
0.9

Sequence for Ion f: R 7_ Ala1_R 2_MeAsp 3_ Arg 4

617.3059

HtyR

0.8

 Structure of MC HtyR:

Counts

0.7

 R 7 = Mdha; R 2 = Hty
 Calc m/z: 617.3042
 Measured: 617.3059 (2.8 ppm mass error)

0.6
0.5
0.4
0.3
0.2
0.1
0
×10 2
2.50
2.25

Unknown
microcystin603.2884

620.3135

 Proposed structure (dm-HtyR) for the unknown compound,

2.0

with desmethylation of Mdha7 to Dha7:

Counts

1.75

 R 7 = Dha; R 2 = Hty
 Calc m/z: 603.2885
 Measured: 603.2884 (0.2 ppm mass error)

1.50
1.25
1.0
0.75
0.50
0.25
0
198

198.5

199
199.5
200
Mass-to-charge (m/z)

200.5

201

Figure 7. Q-TOF spectra of ion f for HtyR and the unknown microcystin. The accurate mass for the f ion observed in HtyR (upper)
matched the calculated mass for the ion containing Hty at position 7 and Mdha at position R3. In contrast, the mass observedfor ion f in the unknown MC matched the calculated mass for the ion containing Hty at position 7 and Dha at position R3.

8

The spectra of the h ion confirmed that minimal desmethylation is occurring in the R3 position in the unknown MC, as its
accurate mass matched the calculated mass for methylated
aspartic acid in position 3, in both the unknown and the HtyR
standard (Figure 8).

×10 2
534.2692

4.0
3.5

 Structure of MC HtyR:

3.0
CountsSequence for Ion h: Ala1_R 2_MeAsp 3_ Arg 4
 R = Hty
 Calc m/z: 534.2671
 Measured: 534.2692 (2.3 ppm mass error)
2

2.5
2.0
1.5
1.0
0.5

525.2403

520.2560

530.2872

0
×10 1
6

534.2683

 Proposed structure (dm-HtyR) for the unknown compound,5with no desmethylation at position 3:

 R 7 = Hty
 Calc m/z: 534.2671
 Measured: 534.2683 (4.0 ppm mass error)

Counts4
32
1529.2782
526.2295

520.3068

0
520

522

524

526
528
530
Mass-to-charge (m/z)

532

534

536

Figure 8. Q-TOF spectra of ion h for HtyR and the unknown microcystin. The accurate mass for the h ion observed in both HtyR
(upper) and the unknown MC matched the calculated mass for the ion containing Hty at position R7 and Mdha at position
R3. In contrast, only a very small peak was observed at nominal mass m/z 520 in both microcystins, indicating that little orno desmethylation was occurring at position R3.

9

In this study, the WHO list of microcystins [4] was used toenter the formulas for 52 microcystins into a PCD, which generates accurate masses for the H+ adducts. The Find by
Formula tool in MassHunter was then used to search forthese accurate masses in the sample total ions chromatogram(TIC) data file. Using this process with the Q-TOF, seven
unknowns were tentatively identified in samples from Lake A
and Lake B, in addition to the desmethyl HtyR (Figures 9 and
10). Additional analysis by Q-TOF MS/MS and comparison to
analytical standards is needed to confirm the presence andidentity of these MCs. Work is on-going to develop an exactmass calculator model to help identify
nontargeted MCs and their variants.

Using a Personal Compound Database to identify
multiple unknown microcystins

Counts

The accurate mass capabilities of the Q-TOF can be used totentatively identify other microcystins that may be present inAlberta lakes, based only on the mass of their H+ adducts.
The first step in the process is to build a Personal Compound
Database (PCD) using the Agilent MassHunter PCDL Manager
Software, which enables the user to create and edit a customizable PCD, including compounds, accurate-mass, andretention time information. One of the advantages of accuratemass scan data is the ability to retrospectively searchacquired data for new compounds using such a
database.
×10 5
7.5
7.0
6.5
6.0
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0

4.66
1,853,141

Lake A

4.84
653,497

4.97
548,460

4.74
422,076
4.59
42,731

4.17 4.24
38,163 24,156
4.0

4.1

4.2

4.3

4.4

4.5

5.12
23,535

4.69
36,788

4.6
4.7
Acquisition time (min)

4.8

4.9

5.0

5.1

5.2

5.3

RT

Compound name

Formula

m/z

Area

Score

Mass diff (ppm)*

4.66

MC-YR

C52 H72 N10 O131045.5346

1,853,141

98.5

-0.7

4.69

MC-LR

C49 H74 N10 O12

995.5549

36,788

90.4

-0.1

4.74

MC-HtyR-DAsp3-Dha7

C51 H70 N10 O13

1031.5185

422,076

96.8

-0.7

4.76

MC-HtyR

C53 H74 N10 013

1059.5501

94,974

98.4

-0.3

4.84

MC-DesMe-LR

C48 H72 N10 O12

981.5405

653,497

99.1

-0.1

4.97

MC-HphR-Dha7

C52 H72 N10 O121029.5400

548,460

99.6

-0.2

4.99

MC-LR-DAsp3-Dha7

C47 H70 N10 O12

967.5236

116,668

94.3

-1.5

4.99

MC-LR

C49 H74 N10 O12

995.5559

32,18592.7-0.6*Mass difference determined by subtracting the theoretical mass of the compound from the calculated mass derived from the Q-TOF analysis, expressed in ppm.
Figure 9. Extracted ion chromatograms of a water sample from Lake A, and a table including retention time (RT), compound name, calculated m/z, and difference of the calculated mass from the theoretical mass for each unknown tentatively identified using the Find by Formula tool in MassHunter and thepersonal compound database (PCD) created for the microcystins in the WHO list. A mass tolerance of 5 ppm and a minimum match score of 70 wereused to make the identification of H+ adducts.

10

×10 61.6

4.81
3,720,594

Lake B

1.4
4.66
3,048,472

1.2Counts

1.0
0.8
0.6

4.13
1,200,312

0.4

4.30
468,4500.2

4.93
425,603

4.50
17,615

0
4.0

4.1

4.2

4.3

4.4

4.5

4.6
4.7
4.8
Acquisition time (min)

4.9

5.06
38,769
5.0

5.1

5.2

5.3

5.4

RT

Compound name

Formula

m/z

Area

Score

Mass diff (ppm)*

4.13

MC-HtyR-DAsp3-Dha7

C51 H70 N10 O13

1031.5193

1,200,312

99.5

-0.4

4.30

MC-LR-DAsp3-Dha7

C47 H70 N10 O12

967.5246

468,450

99.7

-0.3

4.50

MC-LR-DAsp3-Dha7

C47 H70 N10 O12

967.5256

17,615

86.4

0.7

4.66

MC-HtyR-DAsp3-ADMAdda5-Dhb7

C53 H72 N10 014

1073.5305

3,048,472

99.4

0.4

4.68

MC-LR

C49 H74 N10 O12

995.5549

18,83697.7-1.1

3,720,594

99.6

0.5

4.81

MC-LR-DAsp3-ADMAdda5-Dhb7

C49 H72 N10 O13

1009.5359

4.93

MC-LY

C52 H71 N7 O13

1002.5181

425,603

99.4

-0.3

5.06

MC-LR-ADMAdda5

C50 H74 N10 O131023.5505

38,76995.9-0.6*Mass difference determined by subtracting the theoretical mass of the compound from the calculated mass derived from the Q-TOF analysis, expressed in ppm.
Figure 10. Extracted ion chromatograms of a water sample from Lake B, and a table including retention time (RT), compound name, calculated m/z, and difference of the calculated mass from the theoretical mass for each unknown tentatively identified using the Find by Formula tool in MassHunter and thepersonal compound database (PCD) created for the microcystins in the WHO list. A mass tolerance of 5 ppm and a minimum match score of 70 wereused to make the identification of H+ adducts.

of these two technologies supported the hypothesis thatdm-HtyR was present in an Alberta lake water sample.
Databases can be compiled using the Agilent MassHunter
PCDL Manager Software to include the chemical formula for
additional microcystins based on reported analogues in theliterature. Using MassHunter Find by Formula, previously
acquired data files can be retrospectively searched against
PCDL databases and libraries for these additional compounds
as they become known.

Conclusion
When analytical standards are available, the Agilent 1290
Infinity LC System and an Agilent 6460 Triple Quadrupole
LC/MS provides an excellent platform for the targeted analysis of microcystins. Analysis by triple quadrupole LC/MS
using the Agilent 6540 Q-TOF LC/MS System can confirm
suspect compounds using the accurate mass of the molecular
ion adducts, as well as MS/MS fragments. The combination

11

References
1. B.G. Kotak, A.K.-Y. Lam, E. E. Prepas, and S.E. Hrudey.
“Role of chemical and physical variables in regulating
microcystin-LR concentration in phytoplankton ofeutrophic lakes.” Can. J. Fish. Aquat. Sci. 57, 1584–1593(2000).
2. D. M. Orihel, D. F. Bird, M. Brylinsky, H. Chen, D. B. Donald,
D. Y. Huang, A. Giani, D. Kinniburgh, H. Kling, B. G. Kotak,
P. R. Leavitt, C. C. Nielsen, S. Reedyk, R. C. Rooney, S. B.
Watson, R. W. Zurawell, and R. D. Vinebrooke. ” Highmicrocystin concentrations occur only at low nitrogen-tophosphorus ratios in nutrient-rich Canadian lakes.”
Can. J. Fish. Aquat. Sci. 69, 1457-1462 (2012).
3. T. Mayumi, H. Kato, S. Imanishi, Y. Kawasaki,
M. Hasegawa, K. Harada. “Structural characterization of
microcystins by LC/MS/MS under ion trap conditions.”
J. Antibiot (Tokyo) 59, 710-719 (2006).
4. I. Chorus and J. Bartram (Eds.). “Toxic Cyanobacteria in
Water—A Guide to Their Public Health Consequences,
Monitoring, and Management. E & FN Spon, published onbehalf of the World Health Organization, New York (1999).

For More Informatiom
For more information on our products and services visit our
Website at www.agilent.com/chem.

www.agilent.com/chem
Agilent shall not be liable for errors contained herein or for incidental or consequential
damages in connection with the furnishing, performance, or use of this material.
Information, descriptions, and specifications in this publication are subject to change
without notice.

© Agilent Technologies, Inc., 2014
Published in the USA
April 28, 2014
5991-4444EN

Identification and Fragmentation of
Sucralose Using Accurate-Mass
Q-TOF LC/MS and Molecular
Structure Correlator Software
Application Note
Food and Environmental

Authors

Abstract

Imma Ferrer and E. Michael Thurman

The use of Accurate-Mass Q-TOF LC/MS and MS/MS, in both positive and negative

Center for Environmental Masselectrospray ionization (ESI) modes, was evaluated for the identification ofSpectrometry

sucralose in water. Response and fragmentation pathways were investigated.

Department of Environmental

Sucralose responded well using Q-TOF LC/MS in either the positive or negative ion

Engineering

ESI modes. The overall signal intensity obtained in positive ion mode was

University of Colorado,

approximately twice that of negative ion mode.

Boulder, CO 80309

In positive ion mode, sucralose was detected by its sodium adduct [M+Na]+ at

Jerry Zweigenbaum

m/z 419.0038. Accurate mass MS/MS measurements provided structural confirma-

Agilent Technologies, Inc.

tion of the sodiated fragments obtained (m/z 221.0187 and m/z 238.9848). In nega-

2850 Centerville Road

tive ion mode, the deprotonated molecule was observed ([M−H]− at m/z 395.0073).

Wilmington, DE 19808

Fragmentation by MS/MS yielded one characteristic fragment ion (m/z 359.0306).
Agilent MassHunter Molecular Structure Correlator (MSC) software was used to
draw and investigate the fragmentation pathways for the negative and positive ion
MS/MS analyses. The MSC software proved to be a useful tool in assisting with thecharacterization of the fragment ion structures.

Introduction

Experimental

Due to its intense sweetness, noncaloric properties, lowbioaccumulation potential, low toxicity, and the dietaryrequirements of many consumers, sucralose has become oneof the most popular artificial sweeteners used worldwide.
Because the human body does not metabolize sucralose, it
ends up in wastewater and surface water. Current wastewater treatment technologies do not address sucralose, so it
is now ubiquitous in the environment. This is a point of significant concern. A recent study revealed the biological effectsof sucralose in the aquatic environment, which may haveimportant toxicological consequences [1]. For these reasons,
there is growing interest in measuring sucralose in drinking
water, groundwater, surface water, wastewater, and aquatic
environments.

A detailed description of the experimental procedures can befound in the complementary journal article published inAnalytical Chemistry [2].

Standard preparation
Sucralose was purchased from Sigma-Aldrich (St. Louis, MO,
USA). A stock solution of sucralose (1,000 µg/mL) was
prepared in water and stored at −18 °C. From this solution,
working standard solutions were prepared by dilution withmethanol and water.

Instrumentation
The standard was analyzed using an Agilent 1290 Infinity
Binary LC System coupled to an Agilent 6540 Accurate-Mass
Q-TOF LC/MS system with Agilent Jet Stream technology forelectrospray ionization.

Due to its solubility, sucralose is readily analyzed by LC/MS. It
contains three chlorine atoms, which produce a distinctive
chlorine signature when analyzed by MS. Based on many
papers published describing the analysis of sucralose,
LC/MS/MS with multiple reaction monitoring (MRM) in negative ion mode is the most popular method. However, theMRM transitions used are not selective enough to identify
sucralose in water with the same confidence as with accuratemass. The transitions are not discriminatory because they
involve a chlorine loss, which can be present in many othercommon organic molecules.

The HPLC was equipped with a binary pump with an integrated vacuum degasser (G4220A) and an autosampler(G4226A). The HPLC parameters are shown in Table 1.
Table 1.

This application note evaluates the use of an Agilent 6540
Accurate-Mass Q-TOF LC/MS system in both positive andnegative ESI modes for the unequivocal identification of
sucralose in water. Response and the usefulness of molecular
structure correlation software were investigated. The complementary study of Analytical Methodologies for the Detectionof Sucralose in Water in Analytical Chemistry [2] provides adetailed comparison of Q-TOF LC/MS and LC/MS/MS for thedetection of sucralose in environmental water samples.

2

HPLC Parameters

Instrument

Agilent 1290 Infinity Binary LC System

Mobile phases(A) acetonitrile(B) 0.1% formic acid in water

Gradient

Linear: Initial mobile phase composition was 10% A,
held constant for 1.7 minutes, followed by a linear
gradient to 100% A, for a total run time of 10 minutes.

Flow rate

0.4 mL/min

ColumnAgilent ZORBAX Eclipse Plus reversed phase C18
analytical column, 50 × 2.1 mm, 1.8 µm particle size
(p/n 959741-902)

Column temperature

25 °C

Injection volume

20 µL

Q-TOF MS accurate mass spectra were recorded across therange 30−1,000 m/z at 2 GHz. Polarity switching was not
used; samples were injected twice, one under positive ion
mode and the other under negative ion mode. MS/MS experiments were also carried out in both positive and negative ion
modes. The Q-TOF MS and MS/MS parameters are shown in
Table 2.
Table 2.

Results and Discussion
Q-TOF LC/MS results
Figure 1 shows the positive and negative ion ESI mass spectra obtained for the sucralose standard using Q-TOF LC/MS.
In negative ion mode (Figure 1A), the sucralose molecule losta proton, forming the base peak with the exact mass ofm/z 395.0073 (mass error 0.0 ppm). The ions at m/z 397.0045
and 399.0018 correspond to the Cl-37 isotopes of sucralose.

Q-TOF MS and MS/MS Parameters

Instrument

Agilent 6540 Accurate-Mass Q-TOF LC/MS

Ionization mode

Positive and negative ESI with Agilent Jet Stream
technology

Mass range

30–1,000 m/z at 2 GHz

Drying gas temperature

250 °CIn positive ion mode (Figure 1B), the sucralose molecule
adducted a sodium ion, forming the ion with a measured
accurate mass of m/z 419.0040. The measured mass waswithin 0.5 ppm of the exact mass of m/z 419.0038. In thiscase, a protonated molecule was not formed. The two
chlorine isotopes were found at m/z 421.0012 and 422.9988.

10 L/minSheath gas temperature

350 °C

Sheath gas flow rate

11 L/min

×105

Nebulizer gas

45 psi

1.6

Skimmer voltage

65 V

1.4

Octopole RF

750 V

1.2

Fragmentor

190 V

1.0

Capillary

3,500 V

Nozzle1,500 V (negative mode) or 0 V (positive mode)

CountsDrying gas flow rate

[M−H]−

395.0073

Negative ion mode
397.00450.8
0.6
399.0018

0.4

MS/MS parameters
Targeted MS/MS

A

0.2

Precursors: sodiated (positive ion) anddeprotonated molecule (negative ion)

Isolation width

Medium (~4 m/z)

Collision energies

10, 20, and 40 eV

396.0104 398.0073
400.00470392 393 394 395 396 397 398 399 400 401 402 403 404
Mass-to-charge (m/z)
×105

B

3.5

[M+Na]+

419.0040

Positive ion mode
421.0012

3.0

A reference solution containing the internal reference masses(purine (C5H4N4) at m/z 121.0509 and HP-921[hexakis-(1H,1H,3H-tetrafluoro-pentoxy)phosphazene](C18H18O6N3P3F24) at m/z 922.0098 in positive ion mode, andm/z 119.0363 and 966.0007 (formate adduct) in negative ionmode), was delivered by an external quaternary pump.

Counts

2.5
2.0
1.5
1.0
0.5

422.9988
420.0069 422.0049
424.00260
416 417 418 419 420 421 422 423 424 425 426 427 428
Mass-to-charge (m/z)

Stability of mass accuracy was checked daily, and if values
went above 2 ppm error, the instrument was recalibrated.
Figure 1.

Data analysis
The accurate mass Q-TOF MS and MS/MS data wasprocessed using Agilent MassHunter Workstation Software.
Agilent MassHunter Molecular Structure Correlator (MSC)
Software was used to draw and investigate the fragmentation
pathways for the negative and positive ion MS/MS analyses.

3

Results from Q-TOF LC/MS analysis of sucralose in (A) negative
ion mode and (B) positive ion mode, showing measured accuratemasses. In positive ion mode, sucralose was detected as its
sodium adduct [M+Na]+ at m/z 419.0040. In negative ion mode,the deprotonated molecule [M−H]− was observed at m/z 395.0073.

Because the sucralose molecule is hydrogen-rich, allowing a
proton shift as needed, the charged sodium can undergo aneutral loss. This mode of fragmentation can be called asodium migration fragmentation, which is quite rare since
sodium adducts are known to be quite difficult, if not impossible, to fragment to produce structurally significant ions [2].
This phenomenon is not commonly reported for ESI of sodium
adducts, and has been underestimated for the detection of
sucralose using positive ESI.

×105
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0

Counts

ESI−

2

1

Figure 2.

Figure 2 shows the difference in signal obtained from thesucralose standard using positive and negative ESI Q-TOF
LC/MS. The signal intensity obtained in positive ion mode
was approximately twice that of negative ion mode. Still, theratio of signal-to-noise was slightly better in negative ion
mode because background ions were less abundant.

LC/QTOF-MS

ESI+

3

4

5
6
7
Acquisition time (min)

8

9

10

11

Q-TOF LC/MS analysis of the sucralose standard, comparing thesignal intensity obtained in positive and negative ion modes.

×104
3.2
3

221.0190

2.8

238.9853

2.6
2.4

Figure 3 shows the accurate mass spectrum obtained byapplying Q-TOF MS/MS fragmentation to the sodium adductof sucralose (positive ion mode). Two characteristic masses
were obtained: m/z 221.0190 and 238.9853.

2.2Counts

2
1.8
1.6
1.4
1.2

Proposed MS/MS fragmentation pathways

A

Negative ion mode

B

Sucralose

OH OH

C12H18Cl3O8−
Exact mass: 395.0073

C12H19Cl3NaO8+
Exact mass: 419.0038

Cl
O

O
Cl

O
OH

OH

Cl

Cl

0.8
0.6
0.4

203.0081
182.9963
163.0156

0.2
0Figure 3.

Neutral loss
OH
O
Cl

Cl

OH

OH

Cl

HO

Cl
OH OH
C6H10Cl2NaO4+
Exact mass: 238.9848
OH
O

Cl

Cl
O

Neutral loss
Cl
O
Cl
O OH

Cl
OH OH

C12H17Cl2O8−
Exact mass: 395.0306

Figure 4.

Na+
O

OH O

OH
O

341.1202

383.0278

Accurate mass MS-MS spectrum of the sodium adduct ofsucralose (positive ion mode).

− HCl

O

255.0165 290.9907

140 160 180 200 220 240 260 280 300 320 340 360 380 400 420
Mass-to-charge (m/z)

Positive ion mode
Sodium adductOH
Na+ Cl
O
O
O
OH OH
OH OH

OH

419.0040

1

Figure 4 shows the proposed fragmentation pathways fornegative and positive ion MS/MS. In positive ion mode, thesodium adducted sucralose ion splits into two saccharide
fragments, each of which retains the sodium ion. The sodium
ion migrates to either the glucose or fructose side of the molecule, giving rise to the ions at m/z 221.0187 and 238.9848. In
negative ion analyses, sucralose fragments by the loss of HCl,which gives rise to the ion at m/z 359.0306. The chemical
structures were easily confirmed by the Q-TOF accurate massmeasurements with the help of the MSC software.

Na+
OH

OH OH
C6H11ClNaO5+
Exact mass: 221.0187

Proposed fragmentation pathways for negative and positive ion MS/MS of sucralose showing exact masses.

4

well each candidate structure correlates with the MS/MS
spectrum.

As shown in Figures 5 and 6, the fragmentation pathways
were investigated using the MSC software. The software
correlates accurate mass MS/MS fragment ions of thecompound of interest with one or more proposed molecular
structures for that compound. MSC accomplishes this bytrying to explain each observed fragment ion into the proposed
structure using a “systematic bond-breaking”approach.

Because the MSC software does not currently handle
sodiated ions, it was necessary to draw the structure of
sucralose with the sodium on the glucose ring; the upper
structure shown in the middle Figure 5. The fragment ion isshown on the far right. The row in the table on the far right,
highlighted in blue, shows the measured mass of m/z
221.0189 that corresponded to the proposed structure for thatfragment ion.

The input for the MSC software is an accurate mass MS/MS
fragment spectrum, a molecular formula for the compound ofinterest, and candidate molecular structures. The user can
input a molecular formula or structure manually, or select themost probable structure from the possible molecular formulasthat the MSC calculates using the accurate mass MS and
MS/MS information. The MSC then uses the selected
formula, retrieves one or multiple possible structures from a.mol file, an .sdf file, a MassHunter compound database (PCD,
PCDL), or ChemSpider (over the Internet), and scores how

Figure 5.

The structure of sucralose with sodium on the fructose ring
was also drawn using the MSC software (Figure 6). The highlighted portion of the sucralose molecule in the far right box
shows the only proposed structure of the fragment ion at
m/z 238.9852 (the blue highlighted row in the table above thefragment structure.)

MSC analysis showing the fragment of the sodiated molecule of sucralose with the sodium on the glucose ring.

5

Figure 6.

MSC analysis showing the fragment of the sodiated molecule of sucralose with the sodium on the fructose ring.

Figure 7 shows the Q-TOF MS/MS spectrum for sucralose innegative ion mode. The ion at m/z 359.0307 represents theloss of HCl, and is a major fragment ion for this compound.
The ion at m/z 231.9874 is a complex re-arranged sucralosefragment.

Counts

×105
1.4
1.3
231.9784
1.2
1.1
1
0.9
0.8
0.7
397.0038
0.6 59.0137
0.5
0.4
0.3
141.0166
217.0019
359.03070.2
187.9882
313.9461
0.1
101.0249
338.2438
254.9333
161.0464
050 70 90 110 130 150 170 190 210 230 250 270 290 310 330 350 370 390 410
Mass-to-charge (m/z)

Figure 7.

MS/MS spectrum of sucralose (negative ion mode.)6Figure 8 shows the MSC analysis of the fragment ion atm/z 359.0307. Both structures are compatible with the loss of
HCl. Many of the structures in ChemSpider match that ofsucralose; however, for sucralose to be the number one hit in
the MSC software, the structures need to be sorted bynumber of literature references, not the compatibility score(red arrow in Figure 8.)

Figure 8.

MSC analysis of the Q-TOF MS/MS spectrum obtained in negative ion mode. When searching ChemSpider, all structures have same score. Sorting by
number of literature references (# references) raises sucralose to the first (top) position (see red arrow).

7

Conclusions

References

Sucralose responded well using Q-TOF LC/MS when operated in either the positive or negative ESI mode. Sucralose
formed a strong sodium adduct in positive ion mode andreadily lost a proton in negative ion mode. The overall signal
intensity obtained in positive ion mode was approximately
twice that of negative ion mode. As demonstrated in thecomplementary study of detection methodologies forsucralose in water, for the triple quadrupole LC/MS MRM
method, sensitivity was higher in the positive ion mode
(using the two transitions shown in this application note),
than in the negative ion mode [2].

1. A.K.E Wiklund, M. Breitholtz, B.E. Bengtsson, and
M. Adolfsson-Erici “Sucralose – An ecotoxicologicalchallenger?” Chemosphere 2012, 86, 50−55.

Contrary to what is commonly reported for ESI analyses of
sodium adducts, the strong sodium adduct formed in thepositive ion mode was easily fragmented by MS/MS. The
two characteristic accurate mass fragments produced can beused to identify sucralose unequivocally. The MSC software
is a useful tool to assist with the characterization offragment ion structures.

2. I. Ferrer, J.A. Zweigenbaum, and E.M. Thurman“Analytical Methodologies for the Detection of
Sucralose in Water” Analytical Chemistry 2013, 85,9581-9587.

For More Information
These data represent typical results. For more informationon our products and services, visit our Web site atwww.agilent.com/chem.

www.agilent.com/chem
Agilent shall not be liable for errors contained herein or for incidental or consequential
damages in connection with the furnishing, performance, or use of this material.
Information, descriptions, and specifications in this publication are subject to change
without notice.

© Agilent Technologies, Inc., 2014
Printed in the USA
March 6, 2014
5991-4066EN

Sensitive Screening ofPharmaceuticals and Personal Care
Products (PPCPs) in Water Using an
Agilent 6545 Q-TOF LC/MS System
Application Note
Authors

Abstract

Dan-Hui Dorothy Yang1, Mark A.
Murphy , Yue Song , Jimmy Chan2

1

3

As a follow up to the Application Note on detection of pharmaceuticals andpersonal care products (PPCPs) in water using the Agilent 6495 Triple Quadrupole

3

Mass Spectrometer 8, this Application Note describes two methods to screen andAgilent Technologies Inc., 5301
Stevens Creek Blvd, Santa Clara, CA
95051, USA

2

EPA Region 8 Lab, 16194 West 45
Drive, Golden, CO 80403, USA

3

Agilent Technologies Co., Ltd.,
Shanghai, PR China

quantitate PPCPs in water at part per trillion (ppt) levels using the Agilent 6545
Q-TOF LC/MS System. Similarly, the methods were divided into positive ion modeand negative ion mode due to the unique mobile phases used for the two methods.

thThe precise and accurate screening and quantitation of 118 compounds inpositive ion mode and 22 compounds in negative mode was accomplished on the6545 Q-TOF LC/MS using the Swarm tune parameters optimized for small fragileorganic molecules. The high sensitivity slicer mode was selected to maximize
instrument sensitivity. Most of the PPCPs could be detected without tedious
analyte enrichment such as solid phase extraction (SPE). The extent of sample
preparation included filtering approximately 3 mL of sample, adding internal
standards to a 1.0 mL aliquot of the filtered sample, and injecting 40 μL of sample
for analysis by Q-TOF LC/MS with reporting limits for the majority of analytes at
10 ppt. The limit of detection (LOD) and lower limit of quantitation (LLOQ) for mostof the analytes are much lower than 10 ppt.

Introduction
Pharmaceuticals and Personal Care
Products (PPCPs) are comprised ofthousands of chemical substances,including prescription andover‑the‑counter therapeutic drugs,
veterinary drugs, fragrances, andcosmetics. Several studies haveshown that pharmaceuticals andtheir metabolites are present in ourwaterbodies1,2. PPCPs in surface waters
can eventually enter drinking water
systems when treatments are insufficient.
Governmental agencies, such as the EPA
and the European Water Framework, haveproposed regulations to monitor watersupply systems3,4. High performanceliquid chromatography (HPLC) in
combination with high resolution Q-TOF
mass spectrometry is gaining traction
to investigate the occurrence and fateof PPCPs in water systems. There areseveral advantages associated with theanalysis of PPCPs by Q-TOF LC/MS:•

Screening of a large number ofanalytes within one run•

Retrospective data mining for
new analytes•

No need for individual standards
for fragmentation information•

consumption of solvents, and laborious
procedures. PPCPs analysis also has thecomplexity of significant contamination,such as urban surface water sources,
where some of the PPCPs can be foundabove part per billion (ppb) levels. In
addition to higher analyte concentrations,
total organic carbon levels in thesesamples also increase. This can add
substantial interferences to the analytes.
The instrumentation required for PPCPs
analysis must have, not only a broad
dynamic range, but also provide precise
and accurate screening and quantitation
through excellent mass accuracy andresolution.
The Agilent 6545 Q-TOF LC/MS, in
combination with the Agilent Jet Stream
Ionization source, meets the dynamic
analytical demands for the occurrence
and fate of PPCPs in water along with theconvenience of direct sample injection.
Several modifications associated withthe 6545 Q-TOF LC/MS have resulted inhigher analytical performance compared
to previous model. Some of these
improvements include:•

A new slicer design with theoption to operate in high sensitivity
or high resolution mode•

A new high performance highvoltage power supply, along witha new pulser to improve massaccuracy and resolution•

A new enhanced gain-shifted
detector that provides much better
instrument robustness•

A new front end ion optics
for increased precursor ion
transmission

Structure confirmation by
MS/MS fragments

Compared with targeted analysis (forexample, triple quadrupole), Q-TOF LC/MS
has the added benefit of nontargeted or
semitargeted screening for unknowns.
The detection limits for PPCPs in drinking
water are typically in the low part per
trillion (ppt) levels. This poses significant
challenges in analytical methodology
and instrumentation. Sample enrichment
by solid phase extraction (SPE) isoften performed to reach these levels
in drinking water samples5. SPE
requires large sample quantities, high

The most noteworthy change is the
new Particle Swarm Optimization
technology. For the first time, the Particle
Swarm Optimization technology is used
to optimize the 6545 Q-TOF LC/MS

2

mass spectrometers (called Swarm
autotune). Swarm autotune provides
many choices to maximize sensitivity or
mass resolution. First, it can optimize iontransmission at particular mass ranges(for example, 50–250 m/z, 50–750 m/z,
or 50–1,700 m/z) based on applicationneeds. Secondly, the improvements inion transmission for small molecules has
also resulted in enhanced mass accuracy
below 100 m/z. Lastly, instrument
parameters can be tuned according to thefragility of analytes, which requires milder
ion transmission parameters to preserve
their molecular masses. In combination
with the modifications, and the ability toselect the high sensitivity slicer mode, asubstantial increase in signal responsecompared with the previous generation of
the instrument has been achieved6.

Experimental andInstrumentation
Reagents and chemicals

All reagents and solvents were HPLC‑MS
grade. Acetonitrile was purchased from
Honeywell (015-4). Ultrapure water
was obtained from a Milli-Q Integral
system equipped with a LC-Pak Polisher
and a 0.22-μm membrane point-of-use
cartridge (Millipak). Ammonium acetate,
5 M solution, was purchased from
Fluka (09691-250ML). Acetic acid waspurchased from Aldrich (338828-25ML).
The PPCP standards and some of theinternal standards were acquired from an
outside collaborator. The list of analytes
and their internal standards are listed inTable 1 for the positive ion mode method
and Table 2 for the negative ion mode
method. Personal Compound Database
Libraries (PCDLs) for analytes werecreated using the Agilent PCDL Manager(B.07.00) with retention time acquiredwith standards.

Table 1. Analytes and internal standards in positive ion mode method.
Compound

Mass

RT (min)Compound

Mass

RT (min)

10,11-Dihydro-10-hydroxycarbamazepine

254.10553

5

MDMA

193.11028

3.91

6-Acetylmorphine

327.14706

3.72

MDMA-D5

198.14166

3.9

6-Acetylmorphine-D6

333.18472

3.71

Mefenamic acid241.11028

8.15

Acebutolol

336.20491

4.39

Mefenamic acid-D3

244.12911

8.15

Acetaminophen

151.06333

2.92

Meperidine

247.15723

4.98

Acetaminophen-D4

155.08844

2.92

Meperidine-D4

251.18234

4.97

Albuterol239.15214

2.77

Meprobamate

218.12666

5.15

Amitriptyline

277.18305

6.67

Meprobamate-D7

225.17059

5.14

Amitriptyline metabolite

293.17796

5.06

Metformin129.10145

1

Amitriptyline-D3

280.20188

6.66

Methadone

309.20926

6.74

Amphetamine

135.1048

3.6

Methadone-D9

318.26576

6.71

Amphetamine-D5

140.13618

3.57

Methamphetamine

149.12045

3.82

Aripiprazole

447.14803

7.29

Methamphetamine-D11

160.18949

3.78

Aripiprazole-D8

455.19825

7.14

Methotrexate454.17132

3.26

Atenolol

266.16304

2.88

Methotrexate-D3

457.19015

3.26

Atenolol-D7

273.20698

2.87

Methylphenidate

233.14158

4.65

Atorvastatin

558.253

7.51

Methylphenidate-D9

242.19807

4.64

Atrazine

215.09377

7.03

Metoprolol

267.18344

4.53

Atrazine-D5

220.12516

7

Mevastatin390.24062

9.42

Benzoylecgonine

289.13141

4.01

m-Hydroxybenzoylecgonine

305.12632

3.73

Benzoylecgonine-D3

292.15024

4.01

Modafinil

273.08235

5.68

Buprenorphine

467.30356

8.07

Modafinil-D10

283.14512

5.65

Buprenorphine-D4

471.32867

7.72

Monoethylglycinexylidide

206.14191

3.8

Bupropion239.10769

5.33

Montelukast

585.21044

10.88

Caffeine

194.08038

3.6

Morphine

285.13649

2.4

Caffeine-13C3

Carbamazepine

197.09044

3.6

Morphine-D3

288.15532

2.39

236.09496

6.28

Nifedipine

346.11649

7.57

Carbamazepine 10,11 epoxide

252.08988

5.47

Nifedipine oxidized

344.10084

7.48

Carbamazepine-D10

246.15773

6.22

Norfentanyl

232.15756

4.21

Carisoprodol

260.17361

6.75

Norfentanyl-D5

237.18895

4.19

Carisoprodol-D7

267.21754

6.72

Norfluoxetine

295.1184

6.55

Chlorpheniramine

274.12368

5.47

Norfluoxetine-D6

301.15606

6.53

Clenbuterol276.07962

4.6

Normeperidine

233.14158

4.9

Clenbuterol-D9285.13611

4.59

Normeperidine-D4

237.16669

4.89

Clopidogrel carboxylic acid307.04338

4.69

Norquetiapine

295.11432

5.82

Cocaethylene

317.16271

5.42

Norsertraline

291.05815

6.87

Cocaethylene-D3

320.18154

5.41

Cocaine303.14706

4.96

Norsertraline-13C6
Norverapamil297.07828

6.71

440.26751

6.48

Cocaine-D3

306.16589

4.95

Omeprazole

345.11471

5.92

Codeine299.15214

3.4

Oxazepam

286.05091

6.52

Codeine-D6

305.1898

3.39

Oxcarbazepine

252.08988

6.47
3.68

Cotinine

176.09496

3.69

Oxycodone

315.14706

Cotinine-D3

179.11379

3.38

Oxymorphone

301.13141

2.65

DEET

191.13101

7.1

Oxymorphone glucuronide

477.1635

1.13

DEET-D6

197.16867

7.06

Oxymorphone glucuronide-D3

480.18233

1.12

Dehydroaripiprazole

445.13238

6.87

Oxymorphone-D3

304.15024

2.63

Desmethylcitalopram

310.14814

5.81

Paroxetine

329.14272

6.22

Desmethylcitalopram-D3

313.16697

5.81

Paroxetine-D6

335.18038

6.21

3

CompoundMass

RT (min)Compound

Mass

RT (min)

Desmethylvenlafaxine

263.18853

4.6

Phenmetrazine

177.11536

3.74

Desmethylvenlafaxine-D6

269.22619

4.23

Phentermine

149.12045

3.97

Dextromethorphan

271.19361

5.69

Phentermine-D5

154.15183

3.94

Dextromethorphan-D3

274.21244

5.68

Phenylpropanolamine

151.09971

2.93

Diltiazem

414.16133

6.14

Phenylpropanolamine-D3

154.11854

2.93

Diphenhydramine

255.16231

5.88

Pioglitazone

356.11946

7.72

Diphenhydramine-D3

258.18114

5.88

Pregabalin

159.12593

2.73

Disopyramide

339.23106

4.87

Pregabalin-D6

165.16359

2.76

Donepezil

379.21474

5.65

Primidone

218.10553

4.43

Duloxetine

297.11873

6.47

Propranolol

259.15723

5.52

Duloxetine-D3

300.13757

6.47

Propranolol-D7

266.20117

5.5

Ecgonine methyl ester

199.12084

1.15

Pseudoephedrine

165.11536

3.3

Ecgonine methyl ester-D3

202.13967

1.15

Pseudoephedrine-D3

168.13419

3.29

EDDP

277.18305

6.31

Quetiapine

383.16675

6.27

EDDP-D3

280.20188

6.31

Quetiapine-D8

391.21696

6.17

Erythromycin

733.46124

5.78

Ritalinic acid219.12593

3.78

Erythromycin-13C2

Erythromycin-anhydro735.46795

5.78

Ritalinic acid-D10

229.1887

3.75

715.45068

6.3

Sertraline

305.0738

6.88

Escitalopram

324.16379

5.92

Sertraline-D3

308.09264

6.87

Famotidine

337.04493

2.89

Sildenafil

474.20492

6.65

Fentanyl336.22016

5.9

Simvastatin

418.27192

10.4

Fentanyl-D5

341.25155

5.88

Sotalol

272.11946

2.93

Fluoxetine

309.13405

6.7

Sulfamethazine

278.08375

4.45

Fluoxetine-D6

315.17171

6.69

Fluticasone propionate

500.18443

9.05

Sulfamethazine-13C6
Sumatriptan

284.10388

4.45

295.13545

3.5

Gabapentin

171.12593

2.75

Tadalafil

389.13756

6.86

Gabapentin-D10

181.1887

2.72

Temazepam

300.06656

7.2

Glyburide493.14382

8.27

Temazepam-D5

305.09794

7.16

Hydrocodone

299.15214

3.84

Thiabendazole

201.03607

5.18

Hydrocodone-D6

305.1898

3.84

Thiabendazole-13C6

207.0562

5.19

Hydromorphone

285.13649

2.9

Hydromorphone-D3

288.15532

2.89

Tramadol

263.18853

4.6

Tramadol-13C-D3

267.21071

4.58

Hydroxybupropion

255.10261

4.62

Trazadone

371.15129

5.9

Hydroxybupropion-D6

261.14027

4.61

Trazadone-D6

377.18895

5.81

Ketoprofen

254.09429

6.42

Triamterene

253.10759

4.12

Lamotrigine

255.00785

4.73

Trimethoprim

290.13789

3.95

Lamotrigine-13C15N4

259.99935

4.74

258.01792

4.73

Trimethoprim-13C3

Levorphanol

257.17796

Lidocaine
Loratadine
Lorazepam

Lamotrigine-13C3

293.14795

3.94

Tylosin

915.51915

6.12

4.43

Valsartan

435.22704

5.97

234.17321

4.51

Venlafaxine

277.20418

5.19

382.14481

9.38

Venlafaxine-D6

283.24184

5.19

320.01193

6.67

Verapamil

454.28316

6.63

Lorazepam-D4

324.03704

6.67

Zolpidem

307.16846

6.02

MDA

179.09463

3.73

Zolpidem phenyl-4-carboxylic acid337.14264

3.93

MDEA

207.12593

4.18

Zolpidem-D7

314.2124

5.98

4

Table 2. Analytes and internal standards in negative ion mode method.
Compound

Mass

RT (min)Compound

Mass

RT (min)(±)11-Nor-9-carboxy-delta-THC

344.19876

6.568

Diclofenac 4-hydroxy

311.0116

5.067

C12 Triclosan299.99142

6.535

Fenbufen

254.09429

5.317

C3 Ibuprofen

209.14074

5.965

Furosemide

330.00772

4.712

C6 Diclofenac 4-hydroxy317.03173

5.066

Gemfibrozil

250.15689

6.32

C6 Methylparaben

158.06747

4.216

Hydrochlorothiazide

296.96447

3.341

C6 n-Butylparaben

200.11442

5.458

Ibuprofen

206.13068

5.958

C6 Sulfamethoxazole

259.07224

4.096

Methylparaben

152.04734

4.21

C6 Triclocarban

319.99818

6.512

Modafinil acid274.06637

4.619

13
13
13
13
13
13
13

Bezafibrate

361.10809

5.257

Naproxen

230.09429

5.225

Celecoxib

381.07588

5.967

n-Butylparaben

194.09429

5.451

Chloramphenicol

322.01233

4.15

Phenobarbital

232.08479

4.184

D10 Phenytoin

262.15265

4.58

Phenytoin

252.08988

4.6

D4 Diclofenac299.04179

5.87

Pravastatin424.2461

4.326

D5 Chloramphenicol

327.04371

4.14

Sulfamethoxazole

253.05211

4.1

D5 Phenobarbital

237.11618

4.175

Triclocarban

313.97805

6.519

D6 Gemfibrozil

256.19456

6.304

Triclosan

287.95116

6.535

D9 (±)11-Nor-9-carboxy-delta-THC

353.25525

6.546

Warfarin308.10486

5.532

Diclofenac295.01668

5.88

Instrumentation and conditions

Table 3. Agilent 1290 Infinity UHPLC conditions for the positive ion mode method.

•

Parameter

Value

ColumnAgilent ZORBAX Eclipse Plus C18, 2.1 × 100 mm, 1.8 µm (p/n 959758-902)

Agilent 1290 Infinity Standard
Autosampler (G4226A) and sample
cooler (G1330B)

Column temperature

40 °C

Injection volume

40 µL

Speed

Draw 100 µL/min; Eject 200 µL/min

Agilent 1290 Infinity Thermostatted
Column Compartment (G1316C)

Autosampler temperature

6 °C

Needle wash

5 seconds (80 % MEOH/20 % water)

Mobile phase

A) Water with 5 mM ammonium acetate + 0.02 % acetic acidB) Acetonitrile••

Agilent 1290 Infinity Binary Pump
(G4220A)

UHPLC conditions are listed in Table 3
for positive ion mode, and Table 4 fornegative ion mode.

Flow rate

0.3 mL/min

Gradient program

Time
0
0.5
11
13
13.1

Stop time

15 minutes

Post time

1 minute

5

%B
5
5
100
100
5

MS detection

An Agilent 6545 Q-TOF LC/MS with an
Agilent Jet Stream electrospray ionization
source was used.
Jet Stream ionization source parameters
are critical for the sensitive detectionof analytes7. For multiple analyteapplications, parameters are typicallyweighted towards hard‑to‑detect
analytes. In this case, source parameters
were accessed based on the triple
quadrupole data and other studies
on the particular compounds8. Mass
spectrometer source conditions arelisted in Table 5 for the positive ion mode
method, and Table 6 for the negative ion
mode method.

Software•••Agilent MassHunter dataacquisition for Q-TOF mass
spectrometer, Version B.06.01
Agilent MassHunter Qualitative
Software, Version B.07.00 Build
7.0.7024.0
Agilent MassHunter Quantitative
Software, Version B.07.00 Build
7.0.457.0

Table 4. Agilent Infinity 1290 UHPLC conditions for the negative ion mode method.
Parameter

Value

ColumnAgilent ZORBAX Eclipse Plus C18, 2.1 × 100 mm, 1.8 µm (p/n 959758-902)

Column temperature

40 °C

Injection volume

40 µL

Speed

Draw 100 µL/min; Eject 200 µL/min

Autosampler temperature

6 °C

Needle wash

5 seconds (80 % MEOH/20 % water)

Mobile phase

A) Water with 0.005 % acetic acidB) Acetonitrile

Flow rate

0.3 mL/min

Gradient program

Time
0
0.5
6
8
8.1

Stop time

10 minutes

Post time

1 minute

%B
5
5
100
100
5

Table 5. Agilent 6545 Q-TOF LC/MS source parameters for positive ion mode method.
Parameter

Value

Mode

2 GHz Extended dynamic range; high sensitivity slicer mode

Tune

50–250 m/z; Fragile ions

Drying gas temperature

150 °C

Drying gas flow

10 L/minSheath gas temperature

375 °C

Sheath gas flow

11 L/min

Nebulizer pressure

35 psiCapillary voltage

3,500 V

Nozzle voltage

200 V

Fragmentor

125 V

Skimmer

45 V

Oct1 RF Vpp

750 V

Acq mass range

100–1,000 m/z (MS only)

Acq rate

3 spectra/s

Ref mass ions121.050873, 922.009798

6

Dilutions

Stock solutions for analyte standards
and internal standards were prepared at
25 ppb in acetonitrile for each compound.
All samples were fortified with internal
standards at a constant concentrationof 100 ppt, while calibration standards
were spiked at 10 ppt, 25 ppt, 50 ppt,
100 ppt, 250 ppt, 500 ppt, and 1,000 ppt(seven levels) in Milli-Q water.
Two of the three unknown samples
were from an outside collaborator. One
was from a remote site removed fromsignificant anthropogenic sources, andone was from an urban surface water
source. Another sample was freshlycollected local tap water (Santa Clara,
USA). All samples were fortified withinternal standards at 100 ppt after
filtration.

Table 6. Agilent 6545 Q-TOF LC/MS source parameters for negative ion mode method
Parameter

Value

Mode

2 GHz Extended dynamic range; high sensitivity slicer mode

Tune

50–250 m/z; Fragile ions

Drying gas temperature

200 °C

Drying gas flow

12 L/minSheath gas temperature

375 °C

Sheath gas flow

12 L/min

Nebulizer pressure

35 psiCapillary voltage

4,000 V

Nozzle voltage

2,000 V

Fragmentor

110 V

Skimmer

40 V

Oct1 RF Vpp

750 V

Acq mass range

100–1,000 m/z (MS only)

Acq rate

2 spectra/s

Ref mass ions

119.03632, 966.000725

1.0

Results and Discussion

0.8

System stability

0.6
Mass error (ppm)

System stability was evaluated using
300 continuous injections of reserpine
samples at 100 ppb in 70 % acetonitrile
with a gradient of 1.5 minutes. The
acquisition was set to 2 spectra persecond in the presence of internalreference masses (m/z 121.0509
and 922.0098). The mass accuracy
was obtained by Agilent MassHunter
Qualitative Analysis. For all 300 injections,mass accuracy remained very stable,
within 0.25 ppm, as illustrated by Figure 1.
The area %RSD for 300 injections was
2.56 % with three separate sample
preparations.

0.4
0.2
0

-0.2
-0.4
-0.6
-0.8
-1.0

0

50

100

150
Injections

200

250

300

Figure 1. Excellent system stability. Mass accuracy was maintained within 0.25 ppm for 300 continuous
injections of reserpine samples with area %RSD of 2.56 % .

7Increased method performance×105

standards. Data were initially evaluatedin the Agilent MassHunter Qualitative
Analysis Software (B.07.00) using Find by
Formula with a mass error of 5 ppm and aretention time window of ± 0.5 minutes.
Figure 2 shows the responses of the
118 analytes in positive ion mode,and Figure 3 shows the 22 analytes innegative ion mode at 25 ppt.

also prevents organic compounds fromdegrading during ion transmission. All
these factors contribute to the sensitive
detection of PPCPs at low ppt levels
without tedious sample enrichment.
Databases for the positive ion mode
compounds and negative ion mode
compounds were created using the PCDL
manager (B.07.00) with retention time forthe analytes and isotope-labeled internal

The sensitivity of the 6545 Q-TOF LC/MS
was maximized by tuning the instrument
to the 50–250 m/z range for this PPCPs
application, and setting the slicer tohigh sensitivity mode. It is revolutionarythat the user can optimize the iontransmission based on an analyte’s m/z,
especially for midrange mass
spectrometers. The fragile ion option

×105
1.5
Counts

1.201

1.0

1.8

0.5

1.6
5.6

1.4

5.8
6
6.2
Acquisition time (min)Counts

1.2
1.0
0.8
0.6
0.4
0.2
0
1

2

4

3

5
6
Acquisition time (min)

7

8

9

10

6.5

7.0

Figure 2. Signal response in positive ion mode (25 ppt at 40 µL direct injection).
×104
Ibuprofen

×104

Counts

4

Counts

5

5
0

5.9
6.0
6.1
Acquisition time (min)

3

2

1

0
2.5

3.0

3.5

4.0

4.5
5.0
Acquisition time (min)5.5

Figure 3. Signal response in negative ion mode (25 ppt at 40 µL direct injection).

8

6.0

150

No. of analytes detected

140
130

138

139

100 ppt

250 ppt

126

120
110
100

95

80

10 ppt25 ppt50 pptFigure 4. Number of compounds that could be quantified at eachconcentration level with 40 µL direct injection of water samples.
×10 4
2.5

A

×10 4

3.694

CountsCalibration curves

1.25

3.793

2.0

Due to the improvement in mass accuracy
and the increased sensitivity as wellas innate quantitation accuracy of the6545 Q-TOF LC/MS, high confidencecompound identification was achieved
based not only on mass accuracy but
also on isotopic abundance and spacing.
An example is presented in Figure 5.
6-Acetylmorphine was identified withan overall target score of 93.43 out of
100 at 25 ppt in the presence of ~1,000x
coeluting ions.

1.0

0.75

+

329.1564([C19H 21NO 4]+H) +

0.50

0.5

0.25

0

0

3.6

328.1543

B ([C19H 21NO 4]+H)

1.00
Counts

1.5

3.7
3.8
Acquisition time (min)330.1592([C19H 21NO 4]+H) +328.0

328.5 329.0 329.5
Mass-to-charge (m/z)

330.0

Figure 5. Identify with confidence. 6-acetylmorphine at 25 ppt can be detected with high confidence(target score 93.43 out of 100) in the presence of ~1,000x coeluting ions.
A

100
80

Cotininey = 0.004176*x 2 + 1.106211*x – 0.041591
R 2 = 0.9983

60
40
20
0
0

1

2

4
7
3
5
6
Relative concentration

8

9

10

B
Relative responses

Calibration curves were assessed with
PPCPs spiked in Milli-Q water coveringa concentration range from 10 ppt to
1,000 ppt. Some of the analytes hadcorresponding isotope-labeled internal
standards. All samples were fortified
with internal standards at a constantconcentration of 100 ppt. Calibration
curves were generated using a quadratic
fit with a weighting factor of 1/x,
including the origin. The correlation
coefficients (R2) for most of the target
analytes in both polarities were equal toor greater than 0.99; most were greaterthan 0.995, except for methotrexate(R2 = 0.978) and thiabendazole(R2 = 0.984). The calibration curves
for cotinine in positive ion mode andibuprofen in negative ion mode are shown
as examples in Figure 6.

136

90

Relative responses

Most of the compounds can bedetected at a concentration much
lower than 10 ppt without sample
enrichment. Figure 4 shows thenumber of compounds that could bequantified, lower limit of quantitation(LLOQ), at each concentration levelwith accuracy between 80–120 % for atleast three out of five replicates. There
were 140 compounds, 118 in positive
ion mode and 22 in negative ion mode.
Norsertraline was not detected in all
spiked concentrations, probably due todegradation since the stock standard
was over three months old. Of the
44 compounds that failed to be quantified
at 10 ppt, approximately 43 % failed
due to quantitation accuracy beyond
80–120 %.

80

Ibuprofen
y = –0.004667*x 2 + 1.069724*x + 0.015310
R 2 = 0.9989

60
40
20
0
0

1

2

3

4
7
5
6
Relative concentration

8

9

10

Figure 6. Calibration curves of cotinine (positive) and ibuprofen (negative) in Milli-Q water.

9Precision and accuracy

Quantification accuracy on the6545 Q-TOF LC/MS is exceptional
due to excellent mass accuracy andmass resolution. This is reflected by
the number of compounds that can bequantified at low ppt levels withoutsample enrichment. One requirement for
the analytes to be considered detectable
is that the concentration accuracy of atleast three of the five replicates had tobe within 80–120 %. At 10 ppt, 43 % of
44 compounds failed due to quantification
accuracy beyond 80–120 % even though
the signal-to-noise for these analytes
was much greater than 5. Quantification
accuracy was affected more drasticallyat lower levels mainly due to slightbackground influence on peak integration.

44
38

40

No. of analytes

The %RSD calculation was based on
five replicate injections of 138 compounds
made from 10 ppt to 100 ppt(95 compounds at 10 ppt, 31 compounds
at 25 ppt, 10 compounds at 50 ppt,
and two compounds at 100 ppt). The%RSD results are shown in Figure 7.
About 79 % of the compounds could
be quantified with a %RSD of less than
10 %. Only four compounds had elevated%RSD of 20–25 %. These results clearly
demonstrate the precise quantitative
ability of the 6545 Q-TOF LC/MS due tothe modifications, the high sensitivity
slicer mode, and fast data acquisition.

50

30

27

20

16
9

10

4
0

0.7–3.0

3.1–6.0

6.1–10RSD%

10.1–15

15.1–20

20.1–25

Figure 7. Measurement precision of five replicates at LLOQ levels from 10 ppt to 100 ppt; 79 % of analytes
had %RSD less than 10 %.

Table 7. Compounds found in local tap water with positive ion mode method.
Name

Inj 1 (ppt)

Inj 2 (ppt)

Avg. (ppt)

Normeperidine

28.4

30.4

29.4

Temazepam

12.4

13.9

13.2

Table 8. Compounds found in remote source sample with positive ion mode method.
Name

Inj 1 (ppt)

Inj 2 (ppt)

Avg. (ppt)

6-Acetylmorphine

17.7

18.9

18.3

DEET

106.7

107.6

107.1

Temazepam

17.9

18.6

18.2

Real-world samples

Three samples were tested. The first was
freshly collected from local tap water. The
other two samples were from an outside
collaborator, one from a remote site
removed from significant anthropogenic
sources, and the other from an urban
surface water source. Duplicate injections
were run on each sample. The compound
was reported if the average concentrationof the two runs was greater than 10 ppt.
The results are listed in Tables 7–10.
Figure 8 and Figure 9 represent thechromatographs for the two unknown
samples with only 2–3 PPCPs identified.

10

Table 9. Compounds found in an urban surface water sample with positive ion mode method.
Name

Inj 2 (ppt)

Avg. (ppt)

Name

Inj 1 (ppt)

Inj 2 (ppt)

Avg. (ppt)

10,11-Dihydro-10-hydroxycarbamazepine 82.5

Inj 1 (ppt)

82.3

82.4

MDMA

18.5

14.1

16.3

Acebutolol

21.1

20.5

20.8

Meprobamate

105.6

116.5

111.0

Amitriptyline

33.9

35.3

34.6

Metformin2796.12774.42785.3

Atenolol

1590.31476.81533.5

Methadone

42.6

42.4

42.5

Atrazine

40.1

40.2

40.1

Methamphetamine

250.8

249.5

250.1

Bupropion

167.9

160.2

164.0

Metoprolol

426.4

425.3

425.9

Caffeine

719.4

660.1

689.8

Modafinil

21.0

19.8

20.4

Carbamazepine

211.2

219.2

215.2

Monoethylglycinexylidide

44.5

52.3

48.4

Carbamazepine 10,11 epoxide

62.1

56.0

59.1

Norquetiapine

56.6

59.5

58.0

Carisoprodol

31.0

29.9

30.5

Oxazepam

25.6

24.1

24.8

Chlorpheniramine

30.7

29.9

30.3

Oxycodone

94.1

94.6

94.4

Clopidogrel carboxylic acid

144.2

142.8

143.5

Oxymorphone

32.5

31.8

32.2

Cotinine

10.0

10.8

10.4

Phentermine

124.5

121.9

123.2

DEET

564.9

567.2

566.1

Pregabalin

209.3

220.5

214.9

Dehydroaripiprazole

37.1

39.3

38.2

Propranolol

57.8

57.1

57.5

Desmethylcitalopram

100.3

96.8

98.6

Pseudoephedrine

110.8

104.8

107.8

Desmethylvenlafaxine

809.2

834.5

821.9

Ritalinic acid

112.8

123.0

117.9

Dextromethorphan

53.7

49.0

51.4

Sertraline

48.5

49.1

48.8

Diltiazem

76.1

79.4

77.7

Sildenafil

29.6

31.8

30.7

Diphenhydramine

163.5

164.3

163.9

Sotalol

79.3

76.6

78.0

Disopyramide

13.9

14.2

14.0

Temazepam

115.9

110.9

113.4

EDDP

322.8

312.8

317.8

Thiabendazole

76.4

51.9

64.1

Erythromycin

38.5

39.8

39.2

Tramadol

907.5

859.8

883.6

Erythromycin-anhydro

94.1

86.8

90.5

Trazadone

28.6

27.5

28.0

Escitalopram

226.8

225.7

226.3

Triamterene

108.5

113.1

110.8

Fluoxetine

34.3

33.6

33.9

Trimethoprim

269.9

278.1

274.0

Hydrocodone

30.6

31.9

31.3

Tylosin

50.1

48.9

49.5

Hydroxybupropion

165.9

142.1

154.0

Venlafaxine

397.9

405.9

401.9

Levorphanol

184.7

180.3

182.5

Verapamil

29.5

29.4

29.4

Lidocaine

377.8

375.5

376.7

Zolpidem phenyl-4-carboxylic acid

48.9

46.3

47.6

Loratadine

17.3

18.4

17.8

11

No compounds were found in the local
tap water or the remote source water
samples with the negative ion mode
method, however, warfarin was detectedat borderline in the remote source water
sample. The compounds found in theurban surface water sample in negative
ion mode are listed in Table 10.

Table 10. Compounds found in an urban surface water sample with negative ion mode method.

The two surface water samples werealso tested on the Agilent 6495 Triple
Quadrupole LC/MS8, however, thetwo studies were separated by several
months during which the sample
might have degraded. Even so, mostcompounds detected in both the targeted
UHPLC‑Triple Quadrupole method andthe untargeted UHPLC-Q‑TOF method
overlapped well in terms of identified
compounds and corresponding
concentrations. This Application Noteclearly demonstrates that the Agilent
mass spectrometer portfolio can be used
as a complete solution in environmental
testing.

Name

Inj 1 (ppt)

Inj 2 (ppt)

Avg. (ppt)

Celecoxib

40.2

36.6

38.4

Chloramphenicol

8.9

11.9

10.4

Diclofenac

277.2

235.2

256.2

Diclofenac 4-hydroxy

10.0

10.0

10.0

Furosemide

309.3

307.9

308.6

Gemfibrozil

223.7

225.6

224.7

Hydrochlorothiazide

532.7

539.8

536.3

Ibuprofen

47.5

46.8

47.2

Methylparaben

78.6

83.4

81.0

Naproxen

175.4

177.0

176.2

n-Butylparaben

10.2

12.5

11.3

Phenobarbital

43.3

26.2

34.7

Phenytoin

666.4

956.1

811.3

Sulfamethoxazole

649.8

599.2

624.5

Triclocarban

28.2

25.9

27.0

Triclosan

36.4

37.6

37.0

×10 4

Temazepam
×10 4 *301.0738 Area = 64,366

Normeperidine*234.1489 Area = 29,012

3.0

1.4

2.5

Conclusion

1.2
1.0

2.0

Fast and simple Q-TOF LC/MS methods
for the screening of PPCPs in water havebeen developed. The methods leverage
the full advantage of the sensitivity
improvement provided by the hardware
change of the Agilent 6545 Q-TOF LC/MS
System and Swarm autotune on small
fragile molecule ion transmission. The
sensitivity can be further improved by
the selection of the high sensitivity slicer
mode. It has been demonstrated that low
ppt level LLOQs can be achieved for thequantitation of trace contaminants inwater through direct injection. With these
design enhancements, tedious sample
enrichment and cleanup processes can
be avoided. This will increase sample
throughput significantly.

0.8

1.5

0.6

1.0

0.4

0.5

0.2
0

4.7

4.8

4.9

5.0

0

5.1

7.0

7.1

7.2

7.3

7.4

Figure 8. Chromatographs of PPCPs found in local tap water with positive ion method.

×10 4

6-Acetylmorphine
328.1543 Area = 36,096

×10 5

×10 4

Temazepam
301.0738 Area = 68,448

2

1.75

1.75
1.5

1.5
1.25

2.5
2

1.25

1
0.75
0.5

1

1.5

0.75

1

0.5
0.25

0.25
0

DEET
192.1390 Area = 495,701

3.6

3.7

3.8

3.9

4.0

0.5
6.9

7.0

7.1

7.2

7.3

0

7.0

7.1

7.2

7.3

Figure 9. Chromatographs of PPCPs found in remote source sample with positive ion mode method.

12

7.4

References
1. Boyd, G. R.; et al. Pharmaceuticals
and Personal Care Products (PPCPs)
in Surface and Treated Waters ofLouisiana, USA and Ontario, Canada,
Science of The Total Environment
311(1–3), pp 135-149.
2. Snyder, S.; et al. Pharmaceuticals,
Personal Care Products, and Endocrine
Disruptors in Water: Implications for
the Water Industry, Environmental
Engineering Science 2003, 20(5),pp 449-469.
3. EPA Method 1694, Pharmaceuticals
and Personal Care Productsin Water, Soil, Sediment, andBiosolids by HPLC/MS/MS, 2007,
EPA‑821-R-08-002.
4. European Water Framework Directive
2000/60/EC; European Groundwater
Directive 2006/118/EC.
5. Ferra, I.; Thurman, E. M.;
Zweigenbaum, J.; Ultrasensitive
EPA Method 1694 with Agilent
6460 LC/MS/MS with Jet Stream
Technology for Pharmaceutical andPersonal Care Products in Water,
Agilent Technologies Application Note,publication number 5990-4605EN.

6. Yang, D. D.; et al.; Screening andQuantitation of 240 Pesticides
in Difficult Food Matrices Using
the Agilent 6545 QTOF Mass
Spectrometer, Agilent Technologies
Application Note, publication number
5991-5485EN.
7. Cullum, N.; Optimizing Detection of
Steroids in Wastewater Using theAgilent 6490 Triple Quadrupole LC/MS
System with iFunnel Technology,
Agilent Technologies Application Note,publication number 5990-9978EN.
8. Yang, D. D.; Murphy, M. A.; Zhang, S.;
Highly Sensitive Detection of
Pharmaceuticals and Personal Care
Products (PPCPs) in Water Using
Agilent 6495 Triple Quadrupole Mass
Spectrometer, Agilent Technologies
Application Note, publication number
5991-5425EN.

Acknowledgements
The authors would like to thank
Craig Marvin for initiating the project andcoordinating the efforts.

13

www.agilent.com/chem
This information is subject to change without notice.
© Agilent Technologies, Inc., 2015
Published in the USA, June 29, 2015
5991-5954EN

The Use of High Resolution Accurate
Mass GC/Q-TOF and Chemometrics
in the Identification of Environmental
Pollutants in Wastewater Effluents
Application Note
Environmental

Authors

Abstract

Anthony Gravell and Praveen Kutty

A GC/Q-TOF method employing the Agilent 7200 series GC/Q-TOF system andNatural Resources Wales

chemometric analysis tools in Agilent Mass Profiler Professional software has been

Llanelli Laboratory

used to effectively identify environmental pollutants in complex effluent samples

Wales, United Kingdom

from multiple wastewater treatment sites and track their transformation during the

Sofia Nieto
Agilent Technologies, Inc.
Santa Clara, CAUSA

treatment process.

Introduction

Experimental

Efforts to characterize the fate of environmental pollutants
during wastewater treatment are hampered by the large
number of compounds present in various wastewater streams[1]. Untargeted analysis of pesticides and other environmental
pollutants in wastewaters using gas chromatography/mass
spectrometry (GC/MS) can benefit from comprehensive EI
libraries available for this technique, such as NIST 14(containing over 200,000 EI spectra with retention index (RI)
values for over 80,000 compounds). However, the highly complex chromatograms and very large data sets characteristic
for this workflow represent a substantial analytical challenge[2,3].

Instruments
This study was performed on an Agilent 7890B GC system
coupled to an Agilent 7200 series GC/Q-TOF system. The
instrument conditions are listed in Table 1.
Table 1.

GC and Mass Spectrometer Conditions

GC run conditions

While deconvolution of unit mass electron ionization (EI) data
followed by a mass spectral library search is the most typical
workflow used for the identification of environmental pollutants, this approach does not provide enough confidence incompound identification, especially in case of poor library
matching. Using the high resolution, accurate masscapability of GC/Q-TOF provides analysts the required tools
for reliable compound identification.

ColumnAgilent DB-5 MS Ultra Inert,
30 m × 0.25 mm, 0.25 µm film
(p/n 122-5532UI)Injection volume

1 µL

Split ratio

10:1

Split/Splitless inlet temperature

300 °C

Oven temperature program

70 °C for 2 minutes
25 °C/min to 160 °C
3 °C/min to 205 °C
8 °C/min to 280 °C, 7.5 minutes hold
40 °C/min to 325 °C, 2 minutes hold

Carrier gas

Helium at 1.5 mL/min constant flow

Transfer line temperature

300 °C

MS conditions

This application note presents a novel combined untargeted
and targeted approach that uses high resolution accuratemass quadrupole-time-of-flight mass spectrometry(Q-TOF MS) to increase the efficacy of identification of large
numbers of unknown compounds in wastewater. Furthermore,chemometric techniques using Agilent Mass Profiler
Professional (MPP) software are then used for statisticalanalysis and data interpretation to ascertain the fate ofenvironmental pollutants during wastewater treatment.

Ionization mode

EI

Source temperature

280 °C

Quadrupole temperature

150 °C

Mass range

50 to 600 m/z

Sample preparation
Settled primary and final effluent samples from threewastewater treatment works in South Wales, UK, were collected over a period of a few days. Five replicates of final
effluent and primary effluent samples from each of the sites,
as well as blanks, were extracted with dichloromethane, andconcentrated to low volume. Internal standard deuterated
phenanthrene (D10) was added to each sample prior to
extraction.

2

Results and Discussion

Data processing and statistical analysis
The data were processed by chromatographic deconvolution
using the Unknowns Analysis tool in Agilent MassHunter
Quantitative Analysis software (version B.07), followed bytentative compound identification by comparison to the
NIST 14 mass spectral library. The identification of environmental contaminants was further confirmed using the accurate mass tools available in the MassHunter Qualitative
Analysis software (version B.07). A set of approximately 200
putative contaminants of potential interest was then selectedfrom the list of identified components, and semiquantitation
was performed using MassHunter Quantitative Analysis.

Chromatographic deconvolution, library search
and accurate mass confirmation
Using chromatographic deconvolution and NIST library
search, approximately 600 components were tentatively identified in each sample (Figure 2). Compound identity was
further confirmed using accurate mass information, relative
isotope abundance information, and MassHunter accuratemass tools, including Molecular Formula Generator (MFG)(Figure 3). Excellent mass accuracy and small isotope abundance error facilitated confirmation of tentative hits (Table 2).
The data were further processed in MassHunter Quantitative
Analysis software using peak areas normalized to the internal
standard.

The results from quantitation analysis were subsequently
imported and processed in the multivariate statistical package
Mass Profiler Professional (MPP, version 13) to evaluate thetransformation of environmental pollutants in the wastewater
treatment works. Figure 1 outlines the data analysis workflow.

Deconvolution

Library search

Accurate massconfirmation

Visualizationin MPP

Import target
list into MPP

Quant of targets
from deconvolution

Figure 1.

Data analysis workflow.

Figure 2.

The Unknowns Analysis tool was used to perform deconvolution and the NIST library search. The lower middle
panel shows deconvoluted ions selected for the component. They all have the same peak shape, confirming thatthey all belong to the same component, and thus aiding in its identification (triclosan in this case).

3

Figure 3.

Molecular Formula Generator results.

Table 2.

Mass and M+1 Molecular Ion Isotope Abundance Error for Some of the Compounds in the Final Effluent of Site 3

CompoundFormulaAbsolute m/z

Calculated m/z

Mass error for MI* (ppm) M+1 Abundance error (%)

p-Dichlorobenzene

C6H4Cl2145.9684

145.9685

-0.69

0.3

1,2,4-Trithiolane

C2H4S3123.9469

123.947

-0.81

-0.7

2,6-Dichlorophenol

C6H4Cl2O

161.963

161.9634

-2.47

-1.2

Benzothiazole

C7H5NS

135.0142135.0137

3.70

-0.1

3,5-Dichloroaniline

C6H5Cl2N

160.9795

160.9794

0.62

0.8

Chloroxylenol

C8H9ClO

156.0336

156.0336

-0.30

0.7

5-Methylbenzotriazole

C7H7N3133.0636

133.0634

1.00

0.9

2,3,4-Trichloroaniline

C6H4Cl3N

194.9404

194.9404

0.02

-1.5

4-Methyl-1H-benzotriazole

C7H7N3133.0634

133.0634

-0.20

-0.5

Crotamiton

C13H17NO

203.131203.1305

2.46

2.2

Benzophenone

C13H10O

182.0733

182.0726

3.84

0.1

Tonalide (ANTH)C18H26O

258.1981

258.1978

1.16

0.1

1.44

0.76

Average*MI = Molecular ion

Statistical analysis

Principal component analysis

A target list of putative contaminants of potential interest
was chosen, and the quantitation results were subsequently
imported into and processed in the multivariate statisticalpackage MPP to evaluate the transformation of pollutants inthe wastewater treatment plants. The data analysis workflow
is outlined in Figure 1.

Principal Component Analysis (PCA) is a frequently employed
unsupervised multivariate statistical analysis technique fordata dimensionality reduction. PCA analysis revealed distinct
data clusters that represented differences in composition andabundance between all three wastewater treatment plant(WWTW) sites, as well as differences between the primaryand final effluents of each site (Figure 4). The grouping ofdata points along the Z-axis suggests chemical similarity inthe primary effluents for all three sites.

4

400

600

800

200

0

Y-axis-200

Color by effluent
PE Site 1
FE Site 1
PE Site 2
FE Site 2
PE Site 3
FE Site 3
-50

0

50

Heatmap analysis
A heatmap was created in MPP to display the transformationof the environmental pollutants between the primary and final
effluents of the three WWTW plants (Figure 5). A few ofthese compounds are shown in the detailed view on the right.
The abundance of some compounds decreased in the final
effluent relative to the primary effluent. For example, caffeine
and tonalide (AHTN) were significantly decreased in the final
effluents of all three sites while cashmeran (DPMI) did not
significantly change in relative abundance for Sites 1 and 2.
Other compounds actually increased in abundance in the final
effluent versus the primary effluent, such as
2,3,4-trichlorophenol at Site 3.

100

300
300
200
100

200

0

100

-100

0

X-axis-100-50Z-axis

0
0

50

-200

200
400
100 800

Figure 4.

600

PCA plots confirmed the existence of distinct clusters of compounds for the replicate samples of each type of effluent. Primary
Effluent (PE); Final Effluent (FE).

Site 1Site 2Site 3
Condition
Effluent type
Site

Primary
Effluent

Final
Effluent

Primary
Effluent

Final
Effluent

Primary
Effluent

Final
Effluent

Color range
-2.2

0

2.2

CashmeranTonalide
2,3,5,6 -Tetrachloroaniline
Chloroxylenol
Ibuprofen
Caffeine
Atrazine deisopropyl
CodeineCyclic octoatomic sulfur
3,3-Diphenylacrylonitrile
Atrazine desethyl
2,3,4 -Trichloroaniline
Chlorpropham
Trifluralin
Temazepam
Bendiocarb
p-Toluenesulfonamide
3,5-Dichloroaniline
2,3,4 -Trichlorophenol
PhorateTriphenyl phosphate
Hexathiane
Benzene, hexachloro2-Mercaptobenzothiazole
2,2,2-Trichloro-1-phenylethanol
4-Ethylaminophenol

Figure 5.

MPP Heatmap summary (left) and detailed view (right). Columns represent effluent type and treatment site, and the rows
represent compounds.

5

illustrates this analysis for two groups of compounds analyzed at Site 3. In the left panel, all members of the groupdecrease in abundance in a similar manner in the final
effluent with respect to that of the primary effluent.
Conversely, all members of the group in the right panel
increase in abundance in a similar manner in the final effluent. In this way, several groups of compounds with similarchanges in abundance were identified.

K-means clustering analysis
The K-means clustering tool in MPP divides entities(compounds in this case), into groups (clusters) based on
similarity of their behavior under different conditions (primaryand secondary effluents in this case). K-means clusters areconstructed so that the average behavior (increase or
decrease in abundance measured by degree of fold change) in
each group is distinct from any of the other groups. Figure 6

Figure 6.

K-means clustering of two representative groups of compounds from Site 3 that display similar degree offold change in abundance between primary effluent (right side of each graph) and final effluent (left sideof each graph). Some of the compounds present in the left hand cluster are shown in the text box.

6

Venn diagramA

To visually compare treatment sites with respect to thenumber of shared and unique compounds removed or accumulated during the course of the treatment, Venn diagrams
were used (Figure 7). For all the treatment sites, the numberof compounds removed from primary effluents (left side of
Figure 7) was larger than the number accumulated.

Site 1

Compounds reduced in abundance in final effluent

Site 2Conclusions
The combination of gas chromatography, high resolution
Q-TOF mass spectrometry, and chemometrics techniques was
successfully used to characterize and identify environmental
pollutants in complex effluent samples from multiple wastewater treatment sites. Data analysis, using both targeted anduntargeted approaches, revealed a number of compounds,
including pharmaceuticals, benzothiazole-based corrosion
inhibitors and polycyclic musks that were specifically present
in the primary effluents, but were reduced to significantly
lower concentrations in final effluents.

Site 3B

Compounds increased in abundance in final effluent

Site 1The statistical analysis tools in Agilent Mass Profiler
Professional enabled easy and rapid visualization of theresults using multiple statistical approaches that revealed
similarities and differences in treatment modalities between
treatment sites as well as between groups of compounds.
This approach can thus facilitate our understanding of theeffectiveness of wastewater treatment for the removal of
trace organic pollutants.

Site 2Site 3Figure 7.

7

Venn diagrams showing compounds present at significantly
higher levels in primary effluents as compared to the final effluents (A), and those accumulated in final effluents as compared tothe primary effluents (B). The numbers of compounds shared bymultiple sites are depicted by the overlap in the diagrams.

References
1. T. Portoles, E. Pitarch, F. J. Lopez, J. V. Sancho, F. J.
Hernandez. “Methodical approach for the use of GC-TOF
MS for screening and confirmation of organic pollutantsin environmental water.” J. Mass Spectrom. 42(9),1175-85 (2007).
2. T. Portoles, E. Pitarch, F. J. Lopez, F. Hernandez.
“Development and validation of a rapid and wide-scope
qualitative screening method for detection andidentification of organic pollutants in natural water andwastewater by gas chromatography time-of-flight mass
spectrometry.” J. Chromatogr. A 1218(2), 303-15 (2011).
3. F. Hernandez, T. Portoles, E. Pitarch, F. J. Lopez. “Gaschromatography coupled to high-resolution time-of-flightmass spectrometry to analyze trace-level organic
compounds in the environment, food safety andtoxicology.” TrAC Trends in Analytical Chemistry 30(2),388-400 (2011).

For More Information
These data represent typical results. For more information on
our products and services, visit our Web site atwww.agilent.com/chem.

www.agilent.com/chem
Agilent shall not be liable for errors contained herein or for incidental or consequential
damages in connection with the furnishing, performance, or use of this material.
Information, descriptions, and specifications in this publication are subject to change
without notice.

© Agilent Technologies, Inc., 2015
Printed in the USA
December 7, 2015
5991-6488EN

Analysis of Combustion Byproducts
on Firefighter Protection Equipment
Using a Novel High-Resolution
GC/Q-TOF
Application Brief
AuthorIntroduction

Christiane Hoppe-Jones1,

Cancer is a leading cause of fire service morbidity and mortality [1,2]. During afire, firefighters are exposed to smoke and elevated levels of organic chemicals,such as flame retardants, originating from furniture, carpets, and so forth, aswell as their combustion by-products. A high-resolution MS used in full spectrum
acquisition mode is extremely advantageous for the untargeted analysis of theenvironmental contaminants in complex matrices, especially for identificationof trace compounds. To identify polycyclic aromatic hydrocarbons (PAHs) andpolybrominated diphenyl ethers (PBDEs) and other combustion products, we used anovel high-resolution Agilent 7250 GC/Q-TOF.

Shawn Beitel1, Sofia Nieto2 ,Nathan Eno2 , Craig Marvin3, andShane Snyder1
1

Department of Chemical andEnvironmental Engineering,
University of Arizona, Tucson, AZ,

2

Agilent Technologies, Inc.
Santa Clara, CA

3

Agilent Technologies, Inc
Wilmington, DE

Experimental

Br

Br
O

Br

Sample collection and extraction method

Br

Br Br
Br

Samples taken from helmet and neck prior to and post fire

Br
Br

DeBDE
Br
–nBr

4 in2 Area wiped with cotton cloth and IPA

Evaporation of extract to 1 mL

Br

Br

Br

Br

Br

H Br

+H

Br

GC/MS analysis was performed using an Agilent 7890B GC
system coupled to a novel high-resolution (25,000 at m/z 272)
Agilent 7250 GC/Q-TOF, equipped with a low-energy capable
EI source.

Figure 1.

Table 1 shows the instrument parameters.

Instrument Parameters for Agilent 7250 GC/Q-TOF Analysis

Parameter

Value

ColumnAgilent DB-5MS,
0.25 mm × 30 m, 0.25 µm

Injection volume

1 µL

Injection mode

Splitless

Split/Splitless inlet temperature

280 °C

Oven temperature program

50 °C for 3 minutes,
10 °C/min to 300 °C,
7 minutes hold

Carrier gas

Helium at 1.5 mL/min, constant flow

Transfer line temperature

300 °C

Ionization modeStandard EI at 70 eV
Low energy EI at 15 eV and 12 eV

Source temperature,
70 eV/15 eV or less

240 °C/200 °C

Quadrupole temperature

150 °C

Mass range

50 to 1,200 m/z

Spectral acquisition rate

5 Hz

2

Br
Br

Br

–2HBr

Br
Br

Br Br
PBDF
Br

Br

OH Br
Br

Br

Br

BBDD

Br
Br

O

Br

Br

OH Br

Br

–HBr

Br

Br

Analysis

Br

Br

O

Br

Br
O

Br

O

Br

Br

Br

Sample collection and extraction workflow.

+(O)

Br

Br

Table 1.

Br

Br

+H

Br

Br
O

Br

Extraction of cloth with 10 mL DCM twice

Figure 2.

Br

Br

O

Br

Br

O

Br
Br

BBDD

Br
Br

Decomposition of PBDEs, and formation of dioxin and furan
structures.

Data analysis

results, a Quant method was created in Agilent MassHunter
Quantitative Analysis software B.08.00 for semiquantificationof PAHs as well as PAH-like compounds (Figure 4). PBDEs
and molecular ions of unknown brominated compounds wereidentified with the help of low electron energy compound
spectra. Molecular ions were confirmed by evaluating theentire isotopic cluster for m/z, relative isotope abundance,
and isotope ratios using Molecular Formula Generator (MFG)
of MassHunter Qualitative Analysis B.08.00.

The data were acquired using a 7250 accurate mass
high‑resolution GC/Q-TOF system. First, the data were
processed using the feature detecting algorithm SureMass in
Unknowns Analysis B.08.00. Initial compound identification
was performed by spectrum comparison with NIST14 EI
library and confirmed by retention index (RI) matchingwhen possible (Figure 3). Based on Unknowns Analysis

Figure 3.

Unknowns Analysis B.08.00 and spectrum comparison with NIST14.

Figure 4.

Quantitation of PAHs using Agilent MassHunter Quant B.08

3

Results and Discussion

Potential structures of the identified formulas are suggested
in Table 2. These are similar to the PAHs that were detectedby Fernando, et al. [3] in the air and on the skin of firefighters
post fire.

PAH Identification
PAHs were identified in all post fire samples. Over 50 PAH
species were identified on the helmet post fire. Regulated
PAHs that were identified using NIST14 were confirmed byretention index. Table 2 summarizes examples of identified
PAHs and PAH-like compounds on the helmet post fire.
Table 2.

Combined List of Identified PAHs Note: For Most of the PAH and PAH-like Formulas, Multiple Isomers were Identified

Compound/formulam/z

Avg mass
error (ppm)

Regulated PAHs
128.0626

1.01

Acenaphthylene [C12H8]

152.0626

0.82

Acenaphthene [C12H10]

154.07825

0.35

Fluorene [C13H10]

166.07825

1.15

Phenanthrene [C14H10]

178.07825

1.25

178.07825

0.96

Fluoranthene [C16H10]

202.07825

1.06

202.07825

1.51

Benz[a]anthracene [C18H12]228.0939

1.27

228.0939

1.07

Benzo[b]fluoranthene [C20H12]

252.0939

1.81

Benzo[k]fluoranthene [C20H12]

252.09392.23

252.0939

1.79

Indeno[1,2,3-cd]pyrene [C22H12]

276.0939

1.65

278.10955

1.55

276.0939

1.73

Anthracene [C14H10]
Pyrene [C16H10]

Chrysene [C18H12]

Benzo[a]pyrene [C20H12]

Dibenz[a,h]anthracene [C22H14]

Benzo[ghi]perylene [C22H12]

C18H12m/z

Avg mass
error (ppm)

C10H12132.0934

0.91

C11H14146.109

1.07

C11H10142.0777

0.55

C12H16160.1247

1.22

C12H12156.0934

1.07

C13H12168.0934

1.14

C14H14182.109

0.59

C15H12

192.0934

0.53

C15H10190.0777

2.57

C18H22238.1716

1.07

C18H18234.1403

1.04

C17H12216.0934

0.87

C18H10226.0777

0.92

C20H14254.109

1.05

C19H14242.109

0.58

C19H12240.0934

2.17

C22H14278.10955

0.91

C24H18306.1403

1.67

276.0939

2.25

Other PAHs

Naphthalene [C10H8]

C10H8Compound/formula

C12H8

C20H12

C14H10C22H12

C16H10C22H14

C22H12Compound/formulam/z

Avg mass
error (ppm)

O-containing PAHs

C11H10C12H12

4

C13H8O

180.0570.90

218.0726

1.47

C17H10O

230.0726

1.99

C17H10O2

246.0675

1.27

C18H10O

242.0726

2.03

C18H10O2

258.0675

1.15

C20H12O

268.0883

1.68

C16H10OC13H8OC16H10O

C17H10O2

C18H10O

Compound/formulam/z

Avg mass
error (ppm)

C13H9N

N-containing
PAHs

C18H10179.073

1.24

C12H9N

167.073

1.55

C15H9N

203.073

1.52

C17H11N

229.0886

0.63

C19H11N

253.0886

1.08

C13H9N

C15H9NBased on relative abundances, fluoranthrene, pyrene, and thelarger PAHs were the predominant PAHs measured on thehelmet post fire. Figure 5 illustrates the abundances of PAHs
and PAH-like compounds on the helmet post fire.

Figure 6 shows the sum of PAHs, O-containing PAHs, andN-containing PAHs in all samples. The PAHs on the helmet
post fire exceeded the concentration on the skin. The post fireskin samples exhibited greater concentrations of all groups of
PAHs.

Regulated PAHs
Other PAHs
O-Containing PAHs
N-Containing PAHs

×106
10

Abundance

8
6
4
2
0

C10

Figure 5.

C11

C12

C13

C14

C15

C16
C17
C18
No. of carbon atoms

C19

C20

PAHs and PAH-like compounds on post fire wipe of helmet.

×106
50
40

Neck prior
Neck post
Helmet post

30
20
15 %

4

5 ×1074
32
1
5 ×1043

4

Abundance

2

3

36 %

2

164 %

1
NPA
H
Σ

0

1

OPA
H

Abundance

5

Σ

10

Figure 6.

NΣ

PA
H

OPA
H
Σ

Σ

PA
H

0

Total PAHs identified in pre and post fire wipes.

5

C21

C22

C23

C24

Brominated compounds

Other Br-containing compounds were found only on thehelmet post fire, but not on the neck. These are potential
PBDE combustion byproducts (for example, C6H4Br2O) andbrominated PAHs (for example, C16H9Br). Figures 7 and 8
illustrate the mass spectra of a few brominated compounds
at different electron energies. Low electron energies
facilitated the identification of molecular ions of the unknown
compounds.

Brominated compounds were detected only in post firesamples, indicating that they originated from exposure tosmoke.
Isomers of tri-BDE, tetra-BDE, penta-BDE, and hexa-BDE
originating from burned material containing flame retardants
were detected on the neck and helmet post fire. Table 3
summarizes the relative abundances. The retention times
were verified using native standards.

Table 3.

List of Identified PBDE and Other Brominated Compounds Identified in Post Fire Samples

Formulam/z

Mass error Resolving(ppm)*
power

MFG
score

C6H4Br2O

249.8623

1.7

98.34

284.077

0.92

26911

90.92

16.409

n.d.

n.d.

31.17

C7H8Br2N2

277.9049

0.22

27495

88.15

19.891

n.d.

n.d.

5.72

291.8841

0.99

26072

90.08

20.832

n.d.

n.d.

13.84

C10H9BrN2

235.9944

1.27

27136

97.41

21.122

n.d.

n.d.

1.53

C16H9Br279.9882

1.69

26187

98.71

24.609

n.d.

n.d.

4.39

383.9355

0.25

29880

95.36

25.88

n.d.

n.d.

0.32

C12H7Br3O

403.8047

0.45

31609

90.67

22.687

n.d.

1.1

0.29

0.23

29157

96.13

23.063

n.d.

1.41

0.4

C12H6Br4O

481.7152

1.36

30425

91.87

24.835

n.d.

0.9

0.4

2.62

31364

95.40

25.175

n.d.

58.84

13.09

1.15

27322

93.01

25.507

n.d.

0.69

0.3

2.59

30367

95.05

26.684

n.d.

8.99

2.74

2.03

30813

94.57

27.159

n.d.

25.29

9.26

1.24

29448

91.29

27.974

n.d.

1.1

0.35

0.7

30817

91.38

28.335

n.d.

0.83

0.51

1.7

31299

94.73

29.058

n.d.

0.85

0.43

C14H21BrO

C7H6Br2N2O

C15H14Br2O2

C12H5Br5O

C12H4Br6O

559.6257

637.5357

27336

Verified bystandard

Tri-BDE
Tetra-BDE

Penta-BDE

Hexa-BDE

Retention
time (min)Neck: Prefire,% abundance

Neck: Post fire, Helmet: Post fire,% abundance% abundance

13.15

n.d.

n.d.

* Mass error is calculated as a weighted average mass error for the entire isotopic cluster.

6

15.26

Br
O

70 eV
Br

M+

Br

MFG Score

Br

95.40

C12H6Br4O

Figure 7.

15 eV

MFG Score

12 eV

MFG Score

99.47

99.34

Example of PBDE detected on neck wipe post fire. MFG score is based on mass accuracy, isotope spacing as well as relative
isotope abundance ratios.

A
C16H9Br
Candidate structure

227.0729

70 eV

M+[C16H9Br]+[C16H8]+279.9887

200.0621

163.0820[C16H9Br]+ [C16H9Br]+Br[C16H9Br]+

15 eV

281.9862

C6H4Br2O
Candidate structure
[Br]2

175

165

155

B

70 eV
57.0699

185

195

215
225
205
235
Mass-to-charge (m/z)[C16H9Br]+

280.9925 282.9904[C16H9Br]+[C16H9Br]+

279.9880 281.9862

280.9908 282.9893

255

265

275

285

279 280 281 282 283 284 285
Mass-to-charge (m/z)

M+Contaminant ions

71.0854

245[C16H9Br]+[C16H9Br]+ [C16H9Br]+

241.1769

219.2089

167.0343

279.9887 281.9868[C6H4Br2O]+

118.0777

251.8610

15 eV
57.0700

12 eV
57.0704

85.1010160.1242

85.1011160.1248
126.1407

60

Figure 8.

80174.1411[C6H4Br2O]+

251.8603[C6H4Br2O]+

251.8604

100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420
Mass-to-charge (m/z)

Examples of the unknown brominated compounds spectra at different electron energies. Spectra are annotated using MFG tool with Fragment
Formula Annotation. Unannotated ions are likely contaminant ions originated from coeluting compounds. Candidate structures based on 70 eV and
low electron energy spectra are shown on the left.

7Conclusions

References

Neck wipes collected post fire showed that the hood did
not fully protect from smoke-related contaminants. PAHs
as well as PBDEs and other brominated compounds were
found on the neck and helmet of firefighters post fire. Due totheir low detection limits, brominated compounds can act asbiomarkers of exposure of firefighters to smoke.

1. M. N. Bates. “Registry-based case-control study of cancer
in California firefighters” Am. J. Ind. Med. 50 (5), 339-344(2007).

The applied GC/Q-TOF method proved to be very sensitiveand selective in detecting numerous PAHs, PBDEs, and otherbrominated combustion byproducts.

3. S. Fernando. “Evaluation of firefighter exposure to woodsmoke during training exercises at burn houses” Environ.
Sci. Tech. 50, 1536-1543 (2016).

A low-energy-capable EI source facilitated identification of
the molecular ions of the unknown brominated compounds.

For More Information

2. D. Kang, et al. “Cancer incidence among male
Massachusetts firefighters 1987-2003” Am. J. Ind. Med.
51(5), 329-335 (2008).

These data represent typical results. For more information on
our products and services, visit our Web site atwww.agilent.com/chem.

www.agilent.com/chem
Agilent shall not be liable for errors contained herein or for incidental or consequential
damages in connection with the furnishing, performance, or use of this material.
Information, descriptions, and specifications in this publication are subject to change
without notice.
© Agilent Technologies, Inc., 2017
Printed in the USA
July 6, 20175991-8197EN

Application Note

GC/Q-TOF workflows forcomprehensive pesticide analysis

Authors
Christoph Moschet1,
Tarun Anumol2, Philip L. Wylie2,
and Thomas M. Young1
1

2

Department of Civil andEnvironmental Engineering,
University of California,
One Shields Avenue, Davis,
California 95616, USA
Agilent Technologies, Inc.
2850 Centerville Rd.,
Wilmington, DE 19808, USA

Abstract
High-resolution GC/Q-TOF mass spectrometry optimizes simultaneous qualitativeand quantitative screening, providing benefits in throughput and characterization ofenvironmental samples.
This Application Note presents three complementary GC/Q-TOF workflows for thecomprehensive analysis of pesticides and related compounds in environmental
samples:•Target quantification•

Suspect screening using high-resolution accurate mass GC/Q-TOF data•

Nontarget screening using spectral deconvolution and library searching

An Agilent 7200 GC/Q-TOF was used to analyze 51 water samples taken from the
Sacramento/San Joaquin River delta in California before, during, and after two rain
events. After filtering, contaminants in the water extracts were isolated by solid
phase extraction and concentrated by solvent evaporation. The filters were extracted
to recover contaminants bound to particulates. A quantitative analysis method wasvalidated for 21 target pesticides analyzed by the GC/Q-TOF in negative chemical
ionization mode. Sixteen of these target pesticides were found in at least two of
the water extracts. Samples were then re-analyzed using electron ionization (EI).
These data files were processed using Agilent MassHunter Qualitative Analysis
Software using the Find by Formula (FBF) workflow. Samples were screened
for approximately 750 pesticides and related compounds contained in the
Agilent Pesticide Personal Compound Database and Library (PCDL) for GC/Q-TOF.
Accurate mass data provide high selectivity while retention time locking helped toreduce false positive results. Forty‑one additional suspects were identified throughthis technique, with most being confirmed by the analysis of standards. Of these41, 24 were also found by LC/Q-TOF, and 17 compounds were uniquely detected by
GC-EI-Q-TOF.

Nontarget screening used theAgilent MassHunter Unknowns
Analysis Software. This software
first deconvolutes the spectra in thechromatogram, and searches thedeconvoluted components against
a mass spectral library of choice:
NIST14 and the Agilent GC/Q-TOF
Pesticides PCDL. Five pesticides and one
transformation product (TP) not foundby the first two GC-Q/TOF approaches
were tentatively identified in Unknowns
Analysis. In addition, several halogenatedand nonhalogenated organophosphorus
flame retardants, several phenolicantioxidants, and various organohalogen
compounds were tentatively identified.

Introduction
To assess exposure and risk, it isnecessary to monitor micropollutants
in waste, surface, ground, and drinking
water. Traditionally, this has been doneby GC/MS in scan or selected ion
monitoring (SIM) mode. However, forcomplex environmental samples, a single
quadrupole instrument is not sufficiently
selective. A GC triple quadrupole
MS operating in Multiple Reaction
Monitoring (MRM) mode offers muchbetter selectivity, but this techniqueis optimal for targeted compounds
only. Compounds not on the target list
will be missed no matter what theirconcentration. This restricted approach
can lead to inaccuracies in exposureand risk assessment. An ideal scheme
would quantify target compounds forwhich standards are available. It would
also look for a broad range of othercontaminants that can be detected
and quantified once standards become
available.
This Application Note describes three
GC/Q-TOF workflows that have beenused to identify nonpolar and semipolar
micropollutants (for example, pesticides,
and so forth) in surface waters. The first
approach (target method) is to quantify

2compounds for which standards areavailable. In this case, the Q-TOF wasoperated in negative chemical ionization
mode (NCI), as most of the 21 target
compounds were halogenated, thereby
providing optimal sensitivity in NCI
mode. The second approach (suspect
screening) uses EI data with Agilent
MassHunter Qualitative analysis tools
and the Agilent GC/Q‑TOF Pesticides
Personal Compound Database andLibrary (PCDL). The Find by Formulaa
workflow extracts chromatogramsfor the most significant ions for eachcompound at its known retention time.
The number of ions extracted along withthe retention time and mass windows areuser‑settable, as are the requirements
for compound identification. Thisapproach allows users to presumptively
identify compounds without the need foranalytical reference standards.
aIn the most recent version of Agilent MassHunter
Qualitative Analysis Software (B.08.00 Workflows),
this process is called Find by Fragments.

The third approach (nontarget screening)uses Agilent MassHunter Unknowns
Analysis. This software deconvolutes
spectra over the whole chromatogram,and finds individual components withcleaned spectra—that is, spectra whereinterferences have been identified andremoved. Each of the components is
searched against a large mass spectral
library. This work queried the NIST14
unit mass spectral library with spectra
for more than 240,000 compounds, and
the Agilent GC/Q-TOF Pesticides PCDL.
Figure 1 shows the overall workflow
used.
This combined target and suspectpesticide screening GC/Q-TOF workflow
was applied to 51 surface water samples
collected from the Cache Slough in the
Sacramento-San Joaquin River Delta inNorthern California. To get an idea ofother compounds that could be foundin the extracts, six of the samples wereanalyzed using Unknowns Analysis.

Extract samples using a validated
protocol.

Analyze samples by GC/Q-TOF in EI
and NCI modes.

Perform quantitative analysis forcalibrated compounds using NCI
data.

16 Targeted pesticides detected

Perform Find by Fragments
analysis using the Agilent
Pesticides PCDL using EI data.

41 Compounds detected;
33 confirmed with standards

Perform Agilent MassHunter
Unknowns Analysis on EI datausing the NIST14 mass spectral
library and the Agilent Pesticides
PCDL.

Hundreds of compounds detected
including pesticides, metabolites,
halogenated OP fire retardants,
other organohalogen compounds,
and water pollutants

Figure 1. GC/Q-TOF workflow for 1) quantifying target pesticides, 2) screening for hundreds of pesticides
using the Agilent Pesticides PCDL, and 3) screening for other compounds found in the NIST mass spectral
library.

Samples were collected before,
during, and after two rain events. An
article entitled LC- and GC‑Q‑TOF‑MS
as Complimentary Tools for aComprehensive Micropollutant Analysis
in Aquatic Systems1 describes, in detail,the quantitative target method and thequalitative suspect screening method.
Because not all the compounds studied
are amenable to GC/MS analysis,
the research article also describes acomplementary LC/Q-TOF workflow1,2
that provides a comprehensive chemical
profile of the samples.

Experimental
Target compounds and standards
Twenty-one GC-amenable pesticides(Table 1) were included in the targeted
GC/Q-TOF workflow. Most were
pyrethroids, and most contained
halogens, making them good candidates
for analysis by NCI. One internal standard(4,4’-dibromooctafluorobiphenyl) andtwo surrogates (chlorpyrifos D10 andetofenprox D5) were used. For method
validation and quality control, prespiked(before extraction), post spiked (before
injection), and procedural blank(extracted from ultrapure water) samples
were run in triplicate.

Table 1. Target pesticides with their validation results for extracts of water and the filters.

CASRN

MDL (ng/L)

Absolute recovery
water extraction (%)

Absolute recovery filter
extraction (%)

Accuracy(%)

Precision (n = 3)(%)

Bifenthrin

82657-04-3

0.2

73 %

82 %

106 %

1%

Bioallethrin

28434-00-6

0.1

76 %

72 %

111 %

0%

Chlorothalonil

1897-45-6

0.6

94 %

0%

103 %

7%

Chlorpyrifos

2921-88-2

0.1

80 %

62 %

108 %

0%

Cyfluthrin168359-37-5

1.0

–

–

–

–

Cyhalothrin

91465-08-6

0.1

82 %

82 %

110 %

1%

Cypermethrin

65731-84-2

1.0

85 %

62 %

120 %

1%

Cyphenothrin

39515-40-7

0.5

48 %

81 %

113 %

2%

Deltamethrin

52918-63-5

1.0

96 %

66 %

123 %

1%

Esfenvalerate

66230-04-4

0.1

93 %

80 %

118 %

1%

Fipronil

120068-37-3

0.5

92 %

77 %

105 %

3%

–

0.1

98 %

82 %

116 %

0%

CompoundFipronil amide
Fipronil-desulfinyl205650-65-3

0.2

77 %

96 %

87 %

1%

–

0.2

88 %

74 %

247 %

0%

Fipronil-sulfide

120067-83-6

0.1

79 %

89 %

74 %

1%

Fipronil-sulfone

120068-36-2

0.2

91 %

85 %

102 %

3%

Novaluron

116714-46-6

0.05

48 %

91 %

96 %

3%

Permethrin

52645-53-1

2.0

84 %

80 %

113 %

2%

Phenothrin

26002-80-2

5.0

47 %

75 %

123 %

2%

Prallethrin

23031-36-9

0.1

299 %

36 %

81 %

6%

Tetramethrin

7696-12-0

5.0

80 %

205 %

106 %

1%

Fipronil-desulfinyl amide1

Not determined, as reference standard was acquired after the validation experiments.

3

Sample preparation
Fifty-one 1-L samples were collected
at a depth of approximately 30 cm
from the Sacramento-San JoaquinRiver Delta in Northern California
before, during, and after two different
rain events. All the samples werecooled during transport, and stored at
4 °C in the dark until extraction. Water
samples (1 L) were passed through a
GF/F filter, and the filtrate was spiked
with the two surrogates before being
passed through a polymeric solid phaseextraction (SPE) cartridge. After drying
for one hour, the cartridges were eluted
with 10 mL of ethyl acetate. To accountfor losses due to sorption of pyrethroids
to the glass wall, the 1-L containers
were rinsed with dichloromethane(3 × 4 mL). The combined extracts werereduced to 0.2 mL. The GF/F filters
were extracted by sonicating them with
1:1 hexane/acetone (2 × 20 mL), and the
combined filter extracts were reduced
to 0.2 mL. All samples were spiked with
10 ng of 4,4’-dibromooctafluorobiphenyl.
A 10-point calibration curve between
0.1 and 250 ng/mL (in ethyl acetate)
was generated using the same ISTD andsurrogate concentrations.

Table 2. Instrumentation and conditions for analysis.
GC-NCI-MS Method
Injection volume

2.5 µL

Injection mode

Splitless

Purge flow to split vent33 mL/min at 0.75 minutes

Inlet temperature

280 °CGC Settings
ColumnAgilent HP-5MS (30 m × 0.25mm, 025 µm)

Initial oven temperature

100 °C, hold 1 minuteRamp 115 °C/min to 200 °C

Ramp 2

3.8 °C/min to 290 °C

Ramp 3

10 °C/min to 300 °C, hold 4 minutes

He Flow

1.35 mL/min, constant flow

Transfer line temperature

300 °CMS Settings
N2 Collision gas

1.5 mL/min

Reagent gas (methane)

40 %Source temperature

200 °C

Emission current filament

90 µA

Electron energy

70 eV

Acquisition range

35–1,000 m/z

Acquisition speed

3 spectra/sec

Mass calibration

Automated mass calibration after every second sample

Injection volume

2.5 µL

Injection mode

Splitless

Purge flow to split vent33 mL/min at 0.75 minutes

Inlet temperature

280 °C

Sample analysis

ColumnAgilent HP-5MS (30 m × 0.25mm, 025 µm)

Samples were analyzed on an
Agilent 7890B GC coupled to an
Agilent 7200B Q-TOF MS, once by
NCI with methane reagent gas, andonce by EI. Table 2 lists the instrument
conditions.

Initial oven temperature

60 °C, hold 1 minuteRamp 140 °C/min to 120 °C

Ramp 25 °C/min to 310 °C

Optimized He flow for RT locking

0.776 mL/min, constant flow

Transfer line temperature

280 °C

4

GC-EI-MS Method

GC Settings

MS Settings
N2 Collision gas

1.5 mL/min

Source temperature

300 °C

Emission current filament

35 µA

Electron energy

70 eV

Scan range

35–1,000 m/z

Scan speed

4 spectra/sec

Mass calibration

Automated mass calibration after every second sample

Results and Discussion
Target method data processing
and analysis
Target pesticides in all sample
extracts were quantified using
Agilent MassHunter Quantitative Analysis
Software (B.07.00) applied to the NCI
data. The main NCI fragment was usedas the quantifier, and two additional
fragments were used as qualifiers.
Validation results for the 21
GC‑NCI‑Q‑TOF target compounds
showed that 17 had absolute recoveries>70 % in the water extracts, while 15 of
the filter extracts had recoveries >70 %.
Nineteen had accuracies between 70
and 130 %; all 21 had precisions <10 %.
Eighteen had MDLs <1 ng/L (Table 1). Allof the compounds with recoveries lessthan 70 % (phenothrin, cyphenothrin, andprallethrin) were synthetic pyrethroids
that contained no halogens, so theywould be expected to have lower
responses in the NCI mode.

Suspect screening using GC/Q-TOF
with the Pesticides PCDLThe water extracts were rerun in EI
mode to perform a suspect screening
for additional pesticides and related
compounds. The Find By Formula (FBF)
workflow within MassHunter Qualitative
Analysis Software (B.07.00) was applied
to screen for the 750 compoundsb
in the Agilent Pesticides PCDL for
the GC/Q‑TOF. The PCDL contains
curated exact mass spectra for all thecompounds, along with locked retention
times, for the EI GC/MS method shownin Table 2. Locked retention times areavailable for one 20-minute method
and one 40-minute method using
two 15-m columns configured forbackflushing3. A third available method
uses a backflushing configuration with a
5-m column followed by a 15-m column
with locked retention times for a
20-minute run.
b

The GC retention times were firstadjusted to be close to the PCDL values
by adjusting the column flow rate. Then,
retention time locking was performed bymaking five runs: one at the nominal flow
rate, and four more at ±10 % and ±20 %of the nominal flow rate. MassHunter
Acquisition Software automaticallycreates a calibration curve relating thecolumn flow rate to the retention timeof the locking standard. Chlorpyrifos,
a pesticide that elutes near the middleof the chromatogram, was used as thelocking standard. Using the calibration
curve, it does a final adjustment of
the flow rate to bring the RTs for allcompounds within 0.2 minutes of their
PCDL values.

The FBF data mining tool in MassHunter
Qualitative Analysis Software (B.07.00)
used the Pesticides PCDL with the
setpoints shown in Table 3. In this case,five of the most specific ions for eachcompound were extracted from thechromatogram inside a ±0.2-minutewindow around the compound’s locked
retention time and within a specified
mass extraction window. The software
automatically chooses one EIC as areference for the RT and peak shape. Itthen compares the RT and peak shapeof the other four EICs to see if theyfall within the method’s qualification
criteria. In this case, the reference ion
and two more ions must be qualified
for the compound to be listed as ahit. Figure 2 shows the five EICs forboscalid, a fungicide that was found in all
51 samples of river water.

Table 3. Parameters for suspect screening by GC/Q-TOF-MS.
Parameter

Value

SoftwareAgilent MassHunter Qualitative Analysis (B.07.00)

Workflow

Find Compounds by Formula

Values to match

Mass and retention time (retention time optional)

Library

Agilent GC/Q-TOF – Pesticide PCDL (including retention
times). 750 compounds

Extraction algorithm

Agile 2

Match tolerance masses±20 ppm

Retention time tolerance±0.2 minutes

Allowed adducts

pos: - electron

Allowed charge state1

Isotope model

Common organic molecules

Peak spacing tolerance

0.0025 m/z, plus 7 ppm

Scoring (weight)

Mass score: 100
Isotope abundance score: 60
Isotope spacing score: 50
Retention time score: 100

Find by formula score>70 (out of 100)

Absolute height>1,000 counts

Confirm with fragment ions

Molecular ion optional

Number of most specific ions from MS/MS library

5

RT Difference±0.1 minutes

S/N

Not applied

Coelution score>85 (out of 100)

Minimum number of qualified fragments

2

A more recent version of the Pesticides PCDL
for GC/Q-TOF contains entries for more than
850 compounds.
5

1.1
1.0

A

+EIC(342.0321, 344.029...
+EIC(342.0321)+EIC(344.0301)+EIC(139.9898)+EIC(141.9873)+EIC(343.0358)

0.9
0.8

Counts (%)

0.7
0.6
0.5
0.4
0.3
33.3130.2
0.1

33.408

0
-0.1
33.2

Fragment ion/precursor ion ratio

10533.22 33.24 33.26 33.28

33.3

33.32 33.34 33.36 33.38
Acquisition time (min)33.4

33.42 33.44 33.46 33.48+EIC(342.0321)+EIC(344.0301)+EIC(139.9898)+EIC(141.9873)+EIC(343.0358)

B

1
0.5

0.1
33.2

33.22 33.24 33.26 33.28

33.3

33.32 33.34 33.36 33.38
Acquisition time (min)33.4

33.42 33.44 33.46 33.48

Figure 2. A) Extracted ion chromatograms for boscalid found in a sample of river water extract. The normalized coelution
plot (B) shows how the peak shape of the four EICs match the shape of the reference ion. If their peak shapes were thesame, the plot would be a horizontal straight line.

6Figure 3 shows more information
provided by the FBF software including:•

The coelution scores (out of 100) for
each fragment•

The difference between themeasured and theoreticalmonoisotopic molecular ion mass•

The difference between themeasured retention time and thevalue recorded in the PCDL forboscalid

In cases where there is a measurable
molecular ion, the software compares
the theoretical isotope spacing andabundance to the measured values(Figure 4). The molecular ion is notnecessary for compound identification,
as the molecular ion is often notdominant in GC-EI-MS spectra (see theConfirm with fragment ions setting in
Table 3)
The FBF approach identified 41 suspectcompounds (Table 4) that were not on
the target list shown in Table 1. Of these,
33 were unambiguously confirmed byanalyzing a reference standard. For
the additional eight compounds, noreference standard was available, andthey remain tentatively identified.

B

The suspect screening approach
using the FBF workflow allows one
to presumptively identify any of thecompounds contained in the PCDL
without having an authentic standard.
Multiple fragment ions as well as
retention time matching requirements
minimize the possibility of false positive
identifications. This expands the scopeof the analysis from the relatively few
compounds that have standards to themany hundreds of compounds foundin the PCDL. However, unambiguous
confirmation and quantification of thesetentatively identified compounds stillrequires an authentic standard.

C

A

Figure 3. FBF results for boscalid showing: A) coelution scores, B) difference between the measured and theoretical monoisotopic molecular ion mass, andC) difference between the measured and database retention times.

Figure 4. Theoretical (red rectangles) and measured molecular ion isotope pattern for boscalid found in a
Cache Slough water extract.

7

Nontarget screening using
MassHunter Unknowns Analysis
softwareTable 4. Suspect compounds identified by the FBF algorithm.
Compound

8

CAS No.
118-79-6

2,4-Dimethylphenol (2,4-xylenol)

Different uses

105-67-9

2-Methylphenol

Different uses

95-48-7

4-Methylphenol

Different uses

106-44-5

Azoxystrobin

Fungicide

131860-33-8

Boscalid

Fungicide

188425-85-6

Bromacil

Herbicide314-40-9

Carvone1

Insect repellent

99-49-0

Herbicide

1861-32-1

Fungicide121552-61-2

Insect repellent

134-62-3

2,4,6-Tribromophenol

Having screened the samples for all
750 pesticides in the PCDL, it may bedesirable to determine whether there
are any other compounds of interestin the extract. MassHunter Unknowns
Analysis (UA) is designed to perform
this nontarget screening. Unknowns
Analysis deconvolutes the mass spectral
data to isolate cleaned spectra from acomplex mixture of overlapping spectra.
These component spectra are thensearched against a library, and a list ofhits is generated. Since there is no large
comprehensive accurate mass libraryavailable, the NIST unit mass library
was used to generate the broadest
possible list of hits. A new version of thepesticides PCDL (B.08.00), containing
more than 850 compounds, was also
used since it does contain exact mass
spectra. Unknowns Analysis, using
deconvolution, is a different data mining
process than screening using the FBF
workflow, and might identify somecompounds missed by the FBF process.
Table 5 shows the most important
setpoints used for the Unknowns
Analysis.
To determine if there might be otherpollutants of interest in the water
extracts, Unknowns Analysis was applied
to six of the 51 samples. Dependingon the extract analyzed, the number ofdeconvoluted components ranged fromapproximately 1,500 to 4,500. Of these,
between 90 and 325 had NIST library
match factors >70, and approximately
60–120 had match factors >80. An
efficient way to sort through all the hits
was by scrolling down from one hit to
the next while looking at the molecular
structure. When a compound of interest
was observed, the hit was scrutinized tosee if the spectral match looked goodand if the extracted ions appeared tocoelute with good peak shape.

Use
Different uses

1

Chlorthal-dimethyl (Dacthal or DCPA)
Cyprodinil
DEET
Diazinon (Dimpylate)Insecticide333-41-5

Dichlobenil

Herbicide1194-65-6

Dimethenamid (SAN 582H)

Herbicide87674-68-8

Dimethoate

Insecticide60-51-5Diphenylamine1

Fungicide122-39-4

Herbicide

97886-45-8

Herbicide TP–

Dithiopyr
Diuron metabolite [3,4-Dichlorophenylisocyanate]
Eugenol

Insect attractant

97-53-0

Fluridone

Herbicide59756-60-4

Hexazinone

Herbicide51235-04-2

Iprodione (Glycophen)

Fungicide36734-19-7

Malathion

Insecticide121-75-5

Mepanipyrim1

Fungicide110235-47-7

Metolachlor

Herbicide

51218-45-2

Napropamide

Herbicide

15299-99-7

1

Norflurazon

Herbicide23576-24-1

Norflurazon-desmethyl

Herbicide TP

23576-24-1

Omethoate

Insecticide TP

1113-02-6

Oxadiazon

Herbicide

19666-30-9

Herbicide42874-03-3

Oxyfluorofenp,p’-DDE1

Insecticide TP

72-55-9

Herbicide40487-42-1

Different uses

87-86-5

Prodiamine

Herbicide29091-21-2

Propiconazole

Fungicide

60207-90-1

Propyl cresol1

Different uses–

Sulfentrazone

Herbicide122836-35-5

Tebuthiuron

Herbicide34014-18-1

Triclosan

Biocide3380-34-5

Trifluralin

Herbicide

1582-09-8

Pendimethalin
Pentachlorophenol (PCP)11

Tentatively identified (no reference standard comparison available)

The software calculates a component
peak shape quality value (max = 100)and all but a few of the reported hits
had a component shape quality >60.
When using the Pesticides PCDL as thetarget library, retention times had to beclose to the library value to report a hit,
giving further confidence in the peakassignment.

Table 5. Key setpoints for the method used with Unknowns Analysis.
Tab/Parameter1

Setpoint
Peak detection

Peak detection

Deconvolution

SNR Threshold0

Area filters

Absolute area ≥1,000

Deconvolution
RT Window size factor

25, 50, 100, 200

Left m/z delta

Using the UA procedure, 25 pesticides,
three pesticide transformation products(TPs), six organophosphates (threechlorinated flame retardants), and13 other water pollutants (for example,
phenolic antioxidant compounds)
were found and tentatively identified
in the six samples. Five pesticides
and one TP were identified by UA
that were not originally targeted by
the GC/Q-TOF suspect screening
procedure: 2,6-diisopropylnaphthalene,
3,4-dichloroanaline, fenbuconazole,
fluorpyram, fluxapyroxad, and simazine.
The first two were not included in theearlier version of the Pesticides PCDL,and the latter four were targeted by
LC/Q-TOF suspect screening.

0.3 amu or 50 ppm

Right m/z delta

0.7 amu or 50 ppm

Use integer m/z values

Checked for unit mass, unchecked when using 50 ppm

Component shape

Use base peak shape checked

Sharpness threshold

75 %Library search
Libraries

NIST14.L and Pesticides PCDL (ver. B.08.00)

Adjust score

CheckedRemove duplicate hits

Unchecked

Use RT match

Unchecked for NIST, checked for PDCLRT Penalty function

Gaussian (30 seconds)

RT Mismatch penalty

Multiplicative

Max RT penalty

20

Compound identification
Max hit countMin match factor

1
70 for NIST14.L, 20 for PCDLMin m/z30

Library search type

Spectral Search

Target match

Not applied

Blank subtraction
Perform blank subtraction
1

Unchecked

Parameters not listed used the default values

9Figure 5 shows the Unknowns Analysis
results for tris(2,3-dichloropropyl)
phosphate, a flame retardant that was
found in several samples. Because manyorganophosphates have been used asplasticizers or fire retardants, they arewidely distributed in the environment,
and can show up in procedural blanks.

We found triphenyl phosphate, tributyl
phosphate, tris(2-chloroethyl) phosphate,
and tris(2,4-di-tert-butylphenyl)
phosphate in at least one of two
procedural blanks processed by UA.

A

B

D

C

Figure 5. Agilent MassHunter Unknowns Analysis results for a Cache Slough water extract, showing the fire retardant tris(2,3-dichloropropyl) phosphate.
A) TIC (black) and deconvoluted components [green (blue, peak for which results are displayed)]; B) plots of significant EICs overlaid with the component plot;C) molecular structure; D) component spectrum (top) positioned head-to-tail with the library spectrum. The compound had a NIST library match score of 84, and acomponent shape quality of 82.

10

Among the other compounds detectedusing this approach, were an isomer ofdichloronitrobenzene, bis(dichloromethyl)
ether, cyanomelamine, dibenzazepine,
and a trichloronaphthalene isomer. These
compounds remain tentatively identified
because no authentic standard wasavailable for unambiguous confirmation.

Table 6 lists the compounds found using
UA. The first 37 compounds listed inTable 6 were identified with increased
confidence since their retention times
matched the values in the PCDL. The
remaining nine compounds weretentatively identified based only on their
NIST library match score.

Table 6. Pesticides, TPs, and other water pollutants tentatively identified by UA.
Retention time confirmed w/ PCDL

Retention time confirmed w/ PCDL

2-Methylphenol1

Metolachlor14-Methylphenol1

Oxadiazon1

Trichloronaphthalene isomer

Oxyfluorfen1

2,6-Diisopropylnaphthalene

Propiconazole1

3,4-Dichloroaniline (propanil TP)

Simazine

Azoxystrobin1

Sulfentrazone1

Boscalid1

Tributyl phosphate

Bromacil

Trifluralin1

1Chlorothalonil

tris(2-Butoxyethyl)phosphate

Chlorpyrifos2

tris(2,4-Ditertbutyl)phosphate

Cyprodinil1

tris(2.3-Dichloropropyl)phosphateDEETtris(2-Chloroethyl)phosphate

Desmethylnorflurazontris(2-Chloroisopropyl)phosphate2

1

Diazinon1

Compounds with NIST library search >80 but not confirmed with RT

Dimethenamid1

Benzothiazole

Dimethoate1

Dichloronitrobenzene isomer

Dithiopyr1

Benzenesulfonamide, N-butyl-

Diuron mnetabolite (3,4-dichlorophenylisocyanate)1

Benzonitrile

Fenbuconazole

p-Cresol

Fipronil1

Cyanomelamine

Fluorpyrambis(dichloromethyl) ether

Fluridone1

Octodrylene

Fluxapyroxad

Dibenzazepine (iminostilbene)

Hexazinone1
Key:
1
2

Pesticides

Transformation products

Water pollutants

Organophosphates

Found by GC/Q-TOF suspect screening workflow
Found by GC/Q-TOF target analysis

11Conclusions
The Agilent GC/Q-TOF is an ideal tool for
performing both target and nontarget
analysis. River water extracts wereanalyzed in NCI mode for 21 targets(mostly pyrethroids) at sub-ng/Llevels, and 16 of them were found inconcentrations ranging from 0.6 to33 ng/L. The Find by Formula approachpaired with the Agilent Pesticides PCDL
for GC/Q-TOF tentatively identified an
additional 41 suspect compounds, mostof which were confirmed by analyzing
standards. Five of the most significant
ions were extracted around the locked
retention time for each compound in the
database. Hits required that a reference
ion and two additional ions meet the
specified requirements for a hit (Table 3)
and that the retention time matches the
PCDL value. Six of the extracts were
subjected to a nontarget screening
using Agilent MassHunter Unknowns
Analysis, which uses deconvolution topick out cleaned spectra for hundreds

www.agilent.com/chem
This information is subject to change without notice.
© Agilent Technologies, Inc. 2018
Printed in the USA, March 14, 20185991-9132EN

of individual components. These werethen searched against the NIST14 mass
spectral library and the Agilent Pesticides
PCDL. Twenty-five pesticides, threepesticide TPs, six organophosphates,
antioxidants, several halogenatedcompounds, and other pollutants
were found. Most could be tentatively
identified by matching their retention
time to the PCDL.
With the GC-Q/TOF workflows (target,suspect, and nontarget), it was possible
to identify nearly 80 semipolar to
nonpolar water contaminants. Running
the same samples by LC/Q-TOF
identified approximately 100 polar
to semipolar water contaminants1,2.
Although approximately 30 chemicals
from the middle of the polarity range
were detected on both instruments,
this result shows that GC-Q/TOF and
LC-Q/TOF are complementary, and itis necessary to rely on both platforms
to obtain the full contaminant profile inenvironmental samples.

References
1.

Moschet, C.; et al. LC- and
GC‑QTOF‑MS as Complimentary
Tools for a Comprehensive
Micropollutant Analysis in Aquatic
Systems. Environ. Sci. Technol. 2017,
51(3), 1553–1561.

2.

Moshet, C.; Young, T. M.;
Anumol, T. LC/Q-TOF Workflows
for Comprehensive Micropollutant
Analysis. Agilent Technologies
Application Note, publication number
5991-8459EN, October 2, 2017.

3.

Chen, K.; Sanderson, J. Screening ofPesticides and Other Contaminantsin Food Matrices Using a Novel
Highresolution GC/Q-TOF witha Lowenergycapable EI Source.
Agilent Technologies Application
Note, publication number
5991–8170EN, June 16, 2017.

Analysis of Extractable andLeachable (E&L) Compounds
Using a Low-Energy EI-Capable
High‑Resolution Accurate Mass
GC/Q-TOF
Application Brief
Authors

Introduction

Kevin Rowland1, Mark Jordi1, Kai Chen2 ,Accurate compound identification is critical to the study of extractables andleachables (E&L) [1]. The complexity of E&L extracts, containing chemicals witha wide range of classes and concentrations, poses challenges for compound
identification [2]. The GC-amenable portion of E&L studies is conventionallycarried out with a unit mass GC/MS in standard EI full scan mode, with compound
identification through NIST GC/MS library searching. Limited knowledge can beobtained from this technique for those compounds detected without a convincing
library match score.

and Jennifer Sanderson2
1

Jordi Labs
Mansfield, Massachusetts

2

Agilent Technologies, Inc.
Santa Clara, California

This work presents a novel tool to study E&L compounds with enhanced flexibility
and confidence using a high-resolution accurate mass GC/Q-TOF equipped with alow-energy EI capable ion source.

Figure 1.

Agilent 7250 Series GC/Q-TOF system.

Experimental

Data analysis
Compound identification started with Agilent MassHunter
Unknowns Analysis B.08.00 using SureMass signal
processing [3] and matching against the NIST 14 GC/MS
library (Figure 2). The formulas of identified compounds
were studied by comparing the standard EI and low energy
EI spectra. Agilent MassHunter Qualitative Analysis B.08.00
was used to review MS and MS/MS mass spectra whennecessary. The MS/MS spectra-based structure elucidationof the candidates for the unknowns was performed using
Agilent MassHunter Molecular Structure Correlator B.08.00.
Agilent Mass Profiler Professional (MPP) B.13 was used for
differential analysis among sample groups.

Instrumental analysis
The sample extracts and controls were analyzed by an
Agilent 7250 Series GC/Q-TOF system (Figure 1), with
operational conditions listed in Table 1. An injection ofn-alkanes was used to calibrate the retention index (RI) of theacquisition method.
Table 1.

Agilent 7250 GC/Q-TOF Operational Conditions

Parameter

Value

ColumnAgilent DB-5 MS UI, 15 m × 0.25 mm, 0.25 µm

Inlet

S/SL, 310 °C

Carrier gas

1.5 mL/min Helium

Oven program

50 °C for 5 minutes
10 °C/min to 320 °C, 10 minutes

Transferline

280 °C

Source mode

EI, 70 eV, 10-15eV

Source temperature

200 °C

Quad temperature

150 °C

Spectral range

50 to 1,000 m/z

Figure 2.

Agilent MassHunter Unknowns Analysis software for SureMass peak detection and library matching.

2

Sample preparation

Table 2.

A fully assembled single-use bioprocessing system was
extracted using flow-through extraction with saline solution
at 37 °C for 72 hours. The saline solution was preparedby adding one phosphate buffered saline tablet (Sigma) toeach 200 mL of distilled water, resulting in a 137 mH NaCl,
2.7 mM KCl, 10 mM phosphate buffer solution (pH 7.4 at
25 °C). The filter of the device was extracted using ethanol
and water/ethanol (1:1) solutions to demonstrate thedifference between extraction solvents. Control blanks wereprepared for all the extraction experiments. Each extract
solution (except ethanol) was extracted with equal volume ofdichloromethane, then concentrated 10 times for GC/Q-TOF
analysis.

Compound Identification List of Saline Extract (Top List)

CompoundFormula*RI

Mass diff.
(mDa)

Caprolactam

C6H11NO

1,268

0.2

PhenolTri(1,2-propyleneglycol),monomethyl ether
Dowanol 62b isomer 1
Dowanol 62b isomer 2
Dowanol 62b isomer 3
Tentative ID compound
Dowanol 62b isomer 4
Benzoic acid, 4-ethoxy-, ethyl ester
Tentative ID compound
Vanillin

C6H6O

978

0.0

C10H22O4

1,315

0.0

C10H22O4

1,291

-0.2

C10H22O4

1,294

-0.2

C10H22O4

1,289

0.0

C9H12O4

1,572

0.5

C10H22O4

1,286

-0.1

C11H14O3

1,527

0.1

C12H15N3O3

1,659

0.2

C8H8O3

1,399

-0.1

1,144

-0.2

C8H12O3

1,403

0.1

1,908

-0.2

Results and Discussion

Hexanamide

Saline extract versus control blank

7,9-Di-tert-butyl-1-oxaspiro(4,5)deca-6,9- C17H24O3
diene-2,8-dione

Tentative ID compound

We used MPP software to perform the differential analysis
between sample and control, with saline extract results
shown as a representative data set. The results indicate that113 compounds present in saline extract of the complete
device with a fold change ≥3 and a p-value ≥0.05 compared tothe control blank (Figure 3). Table 2 shows the most abundantcomponents.

Tentative ID compound
Ethylparaben
2-Pyrrolidinone, 1-methyl2,4-Di-tert-butylphenol
Tentative ID compound
2-Imidazolidinone, 1,3-dimethylAcetamide, N-cyclohexylButoxyethoxyethanol
Di-t-butylhydroquinone

10

2-Phenylisopropanol
Tentative ID compound8

Benzothiazole
Dimethyl phthalate

6

Tentative ID compound

C15H22O

1,476

0.4

1,522

0.2

C5H9NO

1,040

0.3

1,507

0.0

C8H8O

1,069

-0.2

C5H10N2O

1,109

0.3

C8H15NO

1,292

0.2

C8H18O3

1,187

-0.2

1,467

0.0

C9H12O

1,088

-0.3

C9H10O3

C14H22O

C14H22O2

C5H12O2

1,014

0.1

C7H5NS

1,232

0.2

1,452

0.1

C13H20O2

1,349

0.5

C10H10O4* Formulae of identified compounds were confirmed (or proposed fortentative ID compounds) by comparing the spectra from standard EI andlow-energy EI modes.

4
2

-20

Figure 3.

C6H13NO

-10

0

10

20

Volcano plot revealing compounds significantly present in thesaline extract (upper right).

3

Impact of extraction solvent

Low-energy EI investigation

The filter extracts were evaluated to study the impact of using
different extraction solvents on the overall extractable profile(Figure 4). The Venn diagram enables the easy visualization ofthese results, and shows both the unique as well as common
extractables detected in each extract.

Low-energy EI experiments increase the possibility ofpreserving or confirming the molecular ion (M+) in the
spectrum, as shown in Figure 5. These experiments can offer
additional insights into identifying tentative compounds when
the library search result is not promising.

Object 1: Control_ethanol

Figure 6 illustrates the workflow to study an unknown
compound (common between two solvent extractiongroups) with low-energy EI and Q-TOF MS/MS. The possible
candidate is a benzenemethanol derivative.

Object 4: Control_ethanol/water

Figure 7 shows that the low-energy EI spectra also helped toconfidently identify many alkane compounds unique to theethanol extract.

Conclusions

Object 2: Extract_ethanol

Object 3: Extract_ethanol/water• Accurate mass measurements and RI calibration canenhance confidence in compound identification.

Venn diagram of extractable compounds from the filter of thedevice extracted by different solvents.

• Differential analysis facilitates the comparison of E&L
compounds among sample groups.
×102
1.0

Std EI (70 eV)
Library match

Counts (%)

205.0861

7,9-Di-tert-butyl-1-oxaspiro(4,5)deca-6,9-diene-2,8-dione

0.5

91.0547

175.1121135.0808

261.1484

0
69.0

-0.5

161.0

135.0

91.0

0.8
0.6

261.0

217.0
205.0

60
×102
1.0

232.0

175.0

-1.0

Counts (%)

Figure 4.

• Low-energy EI increases the possibility of preserving
or confirming M+, and accurate mass MS/MS spectra
provide valuable insights into structure elucidation of
unknown compounds.

80

120

100

140

160
180
8
Mass-to-charge (m/z)

2000

220
22

240

260

232.1821

Low energy EI (10 eV)
Formula: C17H24O3
Mass diff: -0.4 ppm

220.1095

175.1118

0.4
0.2
0

Figure 5.

124.0884
60

80

120

100

276.1721

150.1040
140

160
180
Mass-to-charge (m/z)

200

220

240

260

Low-energy EI increases the relative abundance of M+ in the spectrum of a compound confidently
identified with match score of 92.6 (RI: 1908).

4

Step 1: Measure unknowns with low energy EI source
×107

Counts

2.0
1.5
1.0
0.5
0

Counts (%)

×102
1.0

RI: 1,711
17.5

18.0

18.5

19.0
107.0493

Std EI (70 eV)Counts (%)

22.0

191.1434

165.0703

×102
1.0 Low energy EI (10 eV)

135.0807
149.0963

22.5

191.1433

220.1824

220.18250.5
0121.0653

58.0659
50

60

70

80

90

100 110 120 130 140 150 160 170 180 190 200 210 220
Mass-to-charge (m/z)

×104
1.2
1.0
0.8

220.1825[C15H24O]+

Low energy EI (10 eV)
Formula: C15H24O
Mass diff: -1.5 ppm

×104
6
5
4
3
2
1
0

220.1824
CID at 5 eV

135.0803[C9H11O]+

CountsStep 2: Confirm M+ and perform Q-TOF MS/MS

191.1429
149.0960[C H O]+[C10H13O]+ 13 19220.1818[C15H24O]+

121.0645[C8H9O]+

0.6
0.4

221.1852[C15H24O]+

0.2
0

220

220
220
Mass-to-charge (m/z)

×104
6
5
4
3
2
1
0

121.0648

CountsCounts21.5

149.0962

77.0389

55.054521.0

135.0805

121.0649

0.5
0

19.5
20.0
20.5
Acquisition time (min)191.1434
CID at 10 eV

107.0490
77.038660

80

135.0802191.1431

100 120 140 160 180 200 220
Mass-to-charge (m/z)

Step 3: Structure elucidation on possible candidate
0.7 ppm

0.6 ppm

2.4 ppm

0.4 ppm

C10H19O

1.0 ppm
C9H11O

Figure 6.

C8H9O

C10H13O

C7H7O

Study of an unknown compound with low-energy EI and structure elucidation on a possible
candidate using Agilent MassHunter Molecular Structure Correlator.

5

×102
1.2

Counts (%)

1.0

57.0701

Dodecane (n-C12)
Mass diff: -1.2 ppm

0.8
0.6

85.10140.4
98.1093

0.2
0

60

70

80

90

127.1482

100
110
120
130
Mass-to-charge (m/z)

1.2
1.0

171.2063[C12H26]+

170.2031
140

150

160

170

×102

Counts (%)

170.2031[C12H26]+

Eicosane (n-C20)
Mass diff: 0.4 ppm

71.0857

170

171

172

282.3280[C20H42]+0.8
0.6

99.11720.4
0.2
0283.3306[C20H42]+127.1485
183.2107

60

80

100

120

140

282.3280

239.2737

160
180
200
Mass-to-charge (m/z)

220

240

260

280

282

283

284

×102
1.2
Counts (%)

1.0

Hexacosane (n-C26)
Mass diff: 0 ppm

71.0858

366.4220[C26H54]+0.8
0.6

367.4254[C26H54]+

0.4
0.2
0

Figure 7.

267.304360

80

366.4220

100 120 140 160 180 200 220 240 260 280 300 320 340 360
Mass-to-charge (m/z)

366

367

368

Low-energy EI (12 eV) spectra of n-alkanes. M+ clusters show good mass accuracy and isotopic fidelity.

References

For More Information

1. D. Jenke. “Development and Justification of a Risk
Evaluation Matrix to Guide Chemical Testing Necessary To
Select and Qualify Plastic Components Used in Production
Systems for Pharmaceutical Products” PDA J. Pharma.
Sci. Technol. 69, 677–712, (2015).

These data represent typical results. For more information on
our products and services, visit our Web site atwww.agilent.com/chem.

2. A. Mire-Sluis, et al. “Extractable and Leachables.
Challenges and Strategies in Biopharmaceutical
Development” BioProcess Int., Feb (2011).
3. Agilent SureMass, Agilent Technologies Technical
Overview, publication number 5991-8048EN (2017).

www.agilent.com/chem
For Research Use Only. Not for use in diagnostic procedures.
Agilent shall not be liable for errors contained herein or for incidental or consequential
damages in connection with the furnishing, performance, or use of this material.
Information, descriptions, and specifications in this publication are subject to change
without notice.
© Agilent Technologies, Inc., 2017
Printed in the USA
October 17, 20175991-8198EN

Screening of Semivolatile Organic
Compounds (SVOCs) on Aerosol
Particles Using the Agilent 7200
Series GC/Q-TOF System
Application Note
Environmental

Authors

Abstract

Tingting Xu and Xiang Li

The composition of organic compounds absorbed in aerosol particles can provide

Fudan University

important clues concerning aerosol-based air pollution studies. The full acquisition

Shanghai, China

electron ionization (EI) mode of the Agilent 7200 Series Accurate Mass GC/Q-TOF

Shifen Xu
Agilent Technologies (Shanghai) Co., Ltd.
Shanghai, China

MS, in combination with Agilent MassHunter Software tools, enabled a nontargeted
workflow to screen a large variety of compounds in a complex particle extract. The
EI-MS/MS capability was used to study the structure of unknown compounds
based on the accurate mass of product ion fragments.

Kai ChenAgilent Technologies Inc.
Santa Clara, CA

Introduction
Semivolatile organic compounds (SVOCs) exhibit a wide rangeof molecular structures, and correlate to the formation andhealth-related effects of aerosol particles [1,2]. Due to a constantly growing interest in pollution by fine aerosol particles
and an increasing diversity of absorbed organic compounds,
screening of SVOCs has become a more demanding and complex task that requires enhanced selectivity, sensitivity, and anontargeted workflow for data analysis.

PCM
Purgedultimate
union

MMI
inlet

Agilent 7890B GC

Agilent 7200A
GC/Q-TOF

An accurate-mass approach to the analysis of the organic
content of aerosol particles using quadrupole time-of-flightmass spectrometry (Q-TOF) offers more reliable identification,
and allows for a virtually unlimited number of SVOCs to bescreened simultaneously. It provides an ideal analytical toolthat can be used to screen and confirm both target andunknown compounds in complex aerosol particle extracts.
This application note demonstrates a nontargeted screening
workflow for SVOCs absorbed in aerosol particles using ahigh-resolution Agilent 7200 Series GC/Q-TOF system.
Compound hits were obtained by using deconvoluted mass
spectra that were searched against a NIST nominal mass
spectral library. The accurate mass of molecular ion or fragment ions was used in the confirmation of the compound
formula. As an added advantage, the GC/Q-TOF system canbe operated in MS/MS mode to investigate structures of
unknown compounds.

Column 1

Column 2

Agilent HP-5ms UI(15 m × 0.25 mm, 0.25 µm)

Agilent HP-5ms UI(15 m × 0.25 mm, 0.25 µm)

Figure 1.

Agilent 7200 GC/Q-TOF System configuration depicting
midcolumn backflush. The Agilent 7890B GC was coupled to theAgilent 7200A Q-TOF.

Table 1.

Agilent 7890B GC and Agilent 7200A GC/Q-TOF Mass
Spectrometer Conditions

GC conditions
Columns

Agilent HP-5ms UI, 15 m × 0.25 mm, 0.25 µm
film (two each)

Carrier gas

HeliumColumn 1 flow

1.0 mL/min

Column 2 flow

1.2 mL/min

Inlet temperature

280 °CInjection mode

Splitless

Injection volume

2 µL

Oven temperature program 60 °C for 1 minute,
40 °C/min to 120 °C, 0 minutes,
5 °C/min to 310 °C, 10 minutes

Experimental
Instruments
This study was performed using an Agilent 7890B GC system
coupled to an Agilent 7200A Q-TOF system. The instrument
configuration is shown in Figure 1, and the instrument conditions are listed in Table 1. The GC operation enabled retention
time locking (RTL) with the constant flow midcolumn
backflush full screening method included in the Agilent
Pesticides and Environmental Pollutants (P&EP) MRM
database 3.0 (p/n 9250AA).

Run time

50.5 minutes

Backflush

5 minutes (Post run)Transfer line temperature

310 °CQ-TOF MS conditions

2

Ionization mode

EI

Source temperature

300 °C

Quadrupole temperature

180 °C

Mass range

50 to 500 m/z

Spectral acquisition rate

5 Hz, collecting both in centroid andprofile modes

Analysis Software (B.07.01). Molecular Structure Correlator(MSC) Software was used to further study the structures oftentatively identified compounds.

Sample preparation
Aerosol particles (PM2.5) were collected on quartz fiber filters(QFF, Whatman, 5 inch × 8 inch), using samplers (Guangzhou,
China) at a flow rate of 300 L/min. The QFFs were equilibrated
at 20 °C and 40 % relative humidity (RH) in a temperature andhumidity-controlled cleanroom chamber for 24 hours before
and after sampling. The mass of particles on each QFF wasdetermined by an electronic microbalance (Sartorious, IL, US)with 0.001 mg sensitivity. The filter (half) was extracted by
Soxhlet with 50 mL of dichloromethane/hexane (1:1, v/v) at
70 °C for 48 hours. The extract was filtered with a filter
device. After concentration by rotary evaporator, the extract
was further concentrated to 2 mL under a pure N2 stream,with solvent exchanged to n-hexane.

Results and Discussion
Chromatographic peak deconvolution andlibrary search
Data were processed using chromatographic peak deconvolution in the Unknowns Analysis Software with a 100 parts per
million (ppm) accurate mass Extraction Window setting andvariable Retention Time Window Size Factor of 50–200 to findthe highest number of components (Figure 2). The comparison with the NIST library with a Match Factor score > 50
identified approximately 2,600 components including alkanes,
hopanes, ketones, polycyclic aromatic hydrocarbons (PAHs),
oxygenated polycyclic aromatic hydrocarbons (O-PAHs),
esters, and heterocyclic compounds. The Molecular Formula
Generator and Formula Calculator tools were used to confirm
the identity of each compound found by deconvolution.
Screening results of PAHs and O-PAHs are shown in the following text as examples (Figures 3 and 4). A similar workflow
can be applied to screen for other chemical groups.

Data analysis
The data were processed by chromatographic peakdeconvolution using the Unknowns Analysis tool inAgilent MassHunter Quantitative Analysis Software (B.07.01),
followed by compound identification by comparison with the
NIST 14 mass spectral library. The identities of deconvoluted
peaks can be further confirmed using accurate mass information and the accurate mass tools in MassHunter Qualitative

A

B

C

Figure 2.

Unknowns Analysis software was used to perform chromatogram deconvolution. The Total Ion Chromatogram(TIC) (A), mirror plot of component and library hit spectra (B), and overlaid Extracted Ion Chromatograms (EICs) of
the component (C), are depicted.

3

×106
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0

Counts

A

6

14

16

18

20

22 24 26 28 30
Acquisition time (min)

20

25
30
Retention time (min)

32

34

36

38

40

42

44

0
-5
5

10

10

15

35

40

45

C

8
6
4
2

18

8

C 10
12 H
1
C 2
12 H
C 8
13 H
C 10
14 H
C 10
15 H
C 10
15 H
C 12
16 H
C 10
16 H
C 12
16 H
C 14
17 H
C 12
17 H
C 14
17 H
C 16
18 H
C 10
18 H
C 12
18 H
C 18
19 H
C 12
19 H
C 14
20 H
C 12
20 H
C 14
21 H
C 14
22 H
C 12
22 H
C 14
23 H
C 14
24 H
C 12
24 H
C 14
24 H

H
10

C

C

H

0

10

Screening results of PAHs. The chromatogram (A) overlays the TICs (black) and the component
profiles of identified PAHs (red), while the mass errors of molecular ions of the identified PAHs
calculated by the Formula Calculator are shown in B, and formula distribution is shown in C.

A

5
0
-5
5

5
4
3
2
1
0

10

20

25
30
Retention time (min)

35

40

45

O

50

O
12

O
12

H
21

C

2

O

H
20

C

12

10

H
19

O
O

C

O

O

H
18

C

10

H
18

O

3

O

C

12

H
17

C

10

H
17

C

O

O
8

10

H
16

C

2

O

O

O

H
16

C

8

H
O

15

C

10

H
15

C

10

H

2

O

15

C

2

O
8

O
10

H
14

C

O

H
14

C

2

O

10

H
14

C

O
8

O
8

H

C

13

H

2

O
O

13

C

10

O
8

H

H
13

12

C
O

C

O
8

H

O
H

6

C

12

C

15

B

12

-10

3

Mass error (ppm)

12

B

Figure 3.

Number of isomers

10

5

-10

Number of isomers

811

Mass error (ppm)

10

O

O

Example structures

Figure 4.

Confirmation of O-PAHs using accurate mass information. The mass errors of molecular ions of theidentified O-PAHs calculated using the Formula Calculator are shown in A, while formula
distribution and example structures are shown in B.

4

Identification of PAHs and O-PAHs

Structure of an unknown compound proposedby MS/MS

Unknowns Analysis was able to identify a large number of
PAHs that coeluted in the unresolved complex mixture, as
shown in Figure 3. The accurate mass information was used
to confirm approximately 100 PAHs, with mass errors of lessthan 5 ppm. The P&EP MRM database contains retention
times (RTs) for several of the PAHs, and was used to further
confirm hits. The RT differences between database andsample were all within a window of 0.03 minutes. The good
RT match further verifies the use of accurate-mass information as a confirmation tool. The formula distribution shows awide range of PAHs in the extract of aerosol particles, withcarbon numbers from 10 to 28.

The chromatographic peak deconvolution was able to discoverunknown compounds, as shown in Figure 5. The closest
match for this spectrum in the NIST library wasanthra[1,9-cd]pyrazol-6(2H)-one, with a formula of C14H8N2O.
However, this tentative match could be readily rejected basedon mass accuracy alone, since the error on the molecular ion
is 48.62 ppm. This highlights the advantage of accurate mass
data obtained from a Q-TOF versus a unit mass instrument.
Using accurate mass information, the proposed formula forthis unknown compound was C15H8O2, with a mass error of
2.83 ppm. However, no compound with this formula wasfound in the NIST MS library. One of the other advantages of
the 7200 GC/Q-TOF is the ability to perform accurate mass
MS/MS experiments, which are very valuable for structural
elucidation of unknowns.

Similarly, O-PAHs were also identified in the extract of aerosol
particles, with 34 components confirmed by the accuratemass information. Figure 4 indicates the mass errors and theformula distribution of all identified O-PAHs. The structures
for some typical O-PAHs are also displayed in Figure 4.

×105

B
O

CountsNIST library spectrum
C14H8N2O

0

Figure 5.

41
40 50

60

70

80

0.7
0.6
0.5
0.4

0.2

164
76 82

0.8

220.0524
163.0546
192.0576
164.0609
81.5264

0.3

50

50 56 63

0.9Counts

100

220

C

1.0

CountsC14H8N2O (48.62 ppm)
×102 A
220.0524
1.0 Deconvoluted spectrum
Component
RT:
25.1580.8
0.6
163.0546
0.4
192.0576
0.2
81.5264040 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230

102 110118

138 146

169 177

N NH

191

90 100 110 120 130 140 150 160 170 180 190 200 210 220 230

0.1
025.12525.175Acquisition time (min)Comparison of mass spectrum between an unknown compound and a tentative NIST library match (A,B). The coelution profiles ofdeconvoluted ions (C) confirm that they all belong to the same component. However, this compound can be readily rejected based on
mass accuracy alone, since the error on the molecular ion is 48.62 ppm.

5

Figure 6 shows the workflow using the MS/MS mode withaccurate mass fragments to propose the structure of thisunknown compound. The Formula Generator tool was used toassign an accurate empirical formula to the molecular andmajor fragment ions. To propose the structure of thisunknown compound, the spectrum was imported into
Molecular Structure Correlator (MSC) software as a CEF file,×103

A

EI MS/MS
Selecting ion 220, CE 20 eVCounts73.0464
6

C15H8O2 is not included in the NIST MS Library4
2
81.0694
0and MSC searched the ChemSpider database to find all possible structural isomers. Although this type of confirmation is
not completely unambiguous, it provides additional validation
for this tentatively identified O-PAH. Figure 7 illustrates a proposed fragmentation pathway based on fragments listed in
MSC software.

95.0851 107.0849

121.1004

138.0443[C11H6]+164.0614[C13H8]+192.0566[C14H8O]+
176.0624[C14H8]+220.0513[C15H8O2]+
203.0843

70 75 80 85 90 95 100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185 190 195 200 205 210 215 220 225 230
Mass-to-charge (m/z)

B

Figure 6.

Empirical formulas generated from a MS/MS spectrum using the Formula Generator tool (A), and structure elucidation resultsof the compound with an empirical formula of C15H8O2 using MSC software (B). Each individual fragment ion is ranked basedon mass error corresponding to the proposed formula, along with a penalty based on how many bonds needed to be broken to
generate that proposed formula.

1st Candidate(sorted by reference)– CO or CHO

O– CO2 O

– CO OO

O

220.0513
C15H8O2 (2.64 ppm)

Figure 7.

192.0566
C14H8O (1.91 ppm)

163.0537
C13H7 (3.23 ppm)
OO

O

176.0624
C14H8 (-1.98 ppm)

164.0614
C13H8 (3.97 ppm)

Fragmentation pathway of C15H8O2 candidate based on fragments
listed in MSC software.

6

Conclusion
Studies of SVOC compounds absorbed in aerosol particles
can be greatly enhanced by using the multiple features of theAgilent 7200 GC/Q-TOF such as accurate mass information,high sensitivity in full spectrum mode, and MS/MS capabilities. The use of Agilent MassHunter Software capabilities
such as deconvolution, automatic fragment formula annotation, and structure elucidation enabled the nontargeted
approach in SVOC screening. Compound confirmation fromlibrary searches and structure suggestions for unknown
compounds are also important investigative tools.

References
1. U. Pöschl. “Atmospheric aerosols: composition, transformation, climate and health effects” Angew Chem. Int.,
Ed. 44, 7520-7540 (2005).
2. L.B. Liu, et al. “Development of analytical methods forpolycyclicaromatic hydrocarbons (PAHs) in airborne particulates:
A review” J. Environ. Sci. (China), 19, 1-11 (2007).

For More Information
These data represent typical results. For more information on
our products and services, visit our Web site atwww.agilent.com/chem.

7

www.agilent.com/chem
Agilent shall not be liable for errors contained herein or for incidental or consequential
damages in connection with the furnishing, performance, or use of this material.
Information, descriptions, and specifications in this publication are subject to change
without notice.

© Agilent Technologies, Inc., 2015
Printed in the USA
May 19, 2015
5991-5899EN

Analysis of Biomarkers in Crude Oil
Using the Agilent 7200 GC/Q-TOF

Application Note
Petrochemical and Environmental

Authors

Abstract

Frank DavidThe analysis of biomarkers such as (alkyl-) dibenzothiophenes, hopanes, and steranes

Research Institute for Chromatography,

in crude oil is used in many petrochemical applications, including the characterization

Kennedypark 26, B-8500 Kortrijk,of oil sources and the identification of sources of oil spillage. The analysis is normally

Belgium

done by GC-MS after complex sample preparation and fractionation.

Sofia Aronova
Agilent Technologies, Inc.
Santa Clara, CA,
USA

Using a high resolution time-of-flight mass spectrometer, a diluted sample can beanalysed without fractionation and the biomarkers of interest can be measured by
exploiting the high selectivity of ion extraction at accurate mass.
The excellent sensitivity of the system allows the selective detection of dibenzothiophene, alkylated dibenzothiophenes and hopanes. Using the Agilent GC/Q-TOF system
in MS/MS mode, low levels of steranes could be selectively detected.

A crude oil was obtained from Total, France. From the crudeoil, 100 mg was weighed and extracted in 10 mL hexane using
sonication. The solution was centrifuged and an aliquot of theclear supernatant was diluted 10 times in hexane (final oilconcentration: 1 mg/mL)

Introduction
Biological markers or biomarkers include a large group ofhydrocarbons, including alkanes, polycyclic aliphatics, andpolycyclic aromatic hydrocarbons, that are persistent in theenvironment. These biomarkers can be used in many petrochemical applications including the characterization of oilsources, as indicators of oil maturity or oil weathering. The
biomarkers can also be used to identify sources ofenvironmental pollution by oil spillage [1,2].

GC and MS conditions
An Agilent 7890A GC System, equipped with a SSL, combinedwith a 7200 Q-TOF system was used.
The analytical conditions are summarized in Table 1.

Typical biomarkers are heterocyclic polycyclic aromatic hydrocarbons, such as alkyl-dibenzothiophenes, pentacyclic triterpanes such as hopanes, and sterol-derived polycyclic alkanes
such as steranes (for example cholestane). Usually, the analysis of these biomarkers is performed by GC-MS. Prior to theanalysis, sample fractionation by liquid-liquid extraction,column chromatography, and/or solid phase extraction isused to isolate alkane and aromatic fractions. Finally, theextracts are separated by GC, and detection is performed by
MS operated in selected ion monitoring (SIM). Since a large
number of markers are monitored, often multiple runs, eachmonitoring a group of markers might be necessary.

Table 1.
Injection

In this application note, the Agilent 7200 Q-TOF instrument
was used to analyze dibenzothiophenes (DBTs), hopanes, andsteranes in a crude oil by direct injection of a diluted solutionof the oil. The time-of-flight instrument combines high sensitivity and resolution with accurate mass determination. This
offers unique selectivity for the detection of trace analytes in
a complex matrix. The GC/Q-TOF approach is also not limited
to a group of preselected analytes (as in SIM or MRM operation using single quadrupole or triple quadrupole systems),
but the different classes of biomarkers can be detected, identified and quantified by using extracted ion chromatograms at
accurate masses of selective ions. In addition, the possibilities of MS/MS operation also enables additional selectivity attrace level if the selectivity in full scan mode is not sufficient.

GC/Q-TOF Conditions
Inlet type

Split/splitlessMode

SplitlessTemperature

300 °C

Volume1 µL

ColumnDB-5MS, 30 m x 0.25 mm, 0.25 µm

Carrier

1.5 mL/min, helium, constant flow

GC oven

50 °C (1 min) - 10 °C/min - 320 °C (8 min)Detection

Ionization mode

EI

MS modescan 40–500 Da

Acquisition rate

5 Hz

MS/MS mode

scan 40–500 Da
CE : 10 eV

Source temp

280 °C

Quad temp

150 °C

Results and Discussion
First a reference sample, containing 0.38 ng/µL dibenzothiophene was analyzed. The chromatogram (elution window
5–23.5 minutes) is shown in Figure 1. DBT elutes at
16.3 minutes. The mass spectrum is shown in Figure 1B. The
most abundant ion, corresponding to the molecular ion, isdetected at m/z 184.0338. The mass error was less than
2 ppm as compared to the exact mass of molecular ion(C12H8S, M•+ = 184.0341).

Experimental

Next, the crude oil sample was analyzed using the same
method. The total ion chromatogram is shown in Figure 2a.
The profile is characterized by the typical homologue series ofn-alkanes. The elution time of dibenzothiophene is indicatedby an arrow. Using an extracted ion chromatogram at
m/z 184 ± 0.5 amu, as is typically done in single quadrupole
MS systems, dibenzothiophene can be detected, as shown in
Figure 2b. However, several other compounds are also
detected, especially in a time window between 14–18 minutes. These compounds (probably C4-naphthalenes, C14H16,
MW = 184) can potentially interfere with the selectedbiomarker.

Chemicals and Sample
A reference solution NIST SRM 2260a (LGC, Molsheim,
France) containing dibenzothiophene was used to check
instrument performance. The test sample was diluted
10 times in hexane. The final concentration ofdibenzothiophene was 0.38 ng/µL.

2

×10 6A

7
6.5

dibenzothiophene

6
5.5
5
4.5
4
3.5
3
2.5
2
1.5
0
6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

×10 5

B

2.75
2.5
2.25
2
1.75
1.5
1.25
1
0.75
0.5
0.25
0

184.0338

Mass error: -1.75 ppm

139.0536
68.9790
50

Figure 1.

55

60

65

70

92.016779.0091
75

80

85

90

95

152.0615

113.0378

158.0180

100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185 190 195
Counts versus Mass-to-Charge (m/z)

GC/Q-TOF analysis of aromatic hydrocarbon standard mixture. The spectrum obtained for 0.38 ng dibenzothiophene(C12H8S, M•+ = 184.0341) isshown in B.

×10 8
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

Counts versus Acquisition time (min)Figure 2a. Total ion chromatogram of crude oil, elution time of DBT is indicated by arrow.

3

20

21

22

23

24

25

26

27

28

29

×10 6
4.8
4.4

dibenzothiophene

EIC 1844
3.6
3.2

Mass extraction
window: ± 0.5 amu

2.8
2.4

interferences

2
1.6
1.2
0.8
0.4
0
5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

25

26

27

28

29

Counts versus Acquisition time (min)Figure 2b. Extracted ion chromatogram at 184 ± 0.5 amu.

Using an extracted ion chromatogram at exact mass
(184.0341 ± 5 ppm), a much higher selectivity is obtained
allowing to eliminate all the interferences as shown in
Figure 2c. The mass spectrum acquired at 16.32 minutes isshown in Figure 2d. The mass accuracy obtained for DBT in
the complex matrix is not significantly affected(m/z 184.0339) with a mass error below 2 ppm.
×10 6
4
3.6

dibenzothiophene

EIC 184.0341

3.2

Mass extraction
window: ± 5 ppm

2.8
2.4
2
1.6
1.2
0.8
0.4
0
5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Counts versus Acquisition time (min)Figure 2c. Extracted ion chromatogram at 184.0341 ± 5 ppm.

4

21

22

23

24

×106

Mass error in matrix: -1.21 ppm
3.5

184.0339

3
2.5
2
1.5
1
139.05370.5

152.0615

92.0166

71.0852 79.0088

169.1007

0
50

Figure 2d.

55

60

65

70

75

80

85

90

95

100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185 190
Counts versus Mass-to-Charge (m/z)

Mass spectrum of dibenzothiophene in crude oil matrix.

In the same way, it was possible to extract ion chromatograms at m/z 198.0498 for methyl-dibenzothiophenes(C1-DBT, 4 isomers, only three chromatographically resolved)
and at m/z 212.0645 for C2-dibenzothiophenes. These DBT
biomarkers are easily detected as shown in Figure 3.

×10 6

198.0498

5.5
5
4.5

184.03414
3.5

212.0645

3
2.5
2
1.5
1
0.5
0
10.5

Figure 3.

11

11.5

12

12.5

13

13.5

14

14.5 15 15.5 16 16.5 17 17.5
Counts versus Acquisition time (min)

18

18.5

19

19.5

20

20.5

21

21.5

Extracted ion chromatograms at accurate mass (± 5ppm) for the detection of DBT (ion 184.0341), methyl-dibenzothiophenes (ion 198.0498) andC2-dibenzothiophenes (ion 212.0645).

5

Besides the S-containing PAHs, hopanes and steranes arealso important biomarkers. In the same way, extracted ion
chromatograms at accurate mass can be used to selectively
detect these analytes in the complex crude oil matrix. In
Figure 4, the extracted ion chromatograms at 191 ± 0.5 amu(top) and at 191.1794 ± 10 ppm (bottom) are compared. Much
higher selectivity and, consequently, higher signal-to-noiseare clearly obtained by using accurate mass detection.
Several hopanes could be detected in the elution range
between 26 and 30 minutes. Main peaks (27–28 minutes)
probably correspond to nor-hopanes (C29H50, MW=398).
interferences×10 6

hopanes7

191 ± 0.5 amu

6
5
4
3
2
1
0

hopanes
×10 6

191.1794 ± 10 ppm

1
0.8
0.6
0.4
0.2
0
8

Figure 4.

9

10

11

12

13

14

15

16

17 18
19 20 21 22
23Counts versus Acquisition time (min)

24

25

26

27

28

29

30

31

Extracted ion chromatograms at 191 ± 0.5 amu (top) and at accurate mass (191.1794 ± 10ppm) for the detection of hopanes.

6

32

33

34

Finally, specific ions for steranes were extracted. As theseanalytes are present at lower concentration in this sample
and interfered with by matrix ions even when using accuratemass EICs, as illustrated in Figure 5. Since the GC/Q-TOF also
allows the operation in MS/MS mode, the analysis was
repeated using ion 400 (M•+ for C29H52 = ethylcholestane) asa precursor ion. The EIC of product ion at 217.1951 now shows
improved selectivity of detection for the ethylcholestane
steranes, as shown in Figure 6.
×10 5
0.9

EIC 217.1951

0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
×10 4
2.4
2.2
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0

EIC 400.4064

13

Figure 5.

14

15

16

17

18

19

20
21
22
23
24
25
Counts versus Acquisition time (min)

26

27

28

29

30

Extracted ion chromatograms at accurate masses (217.1951 ±10ppm and 400.4064 ± 10 ppm) for the detection of steranes.

7

31

32

MS: EIC 400.4064

×10 4
2
0.8
0.6
0.4
0.2
1
0.8
0.6
0.4
0.2
0
×10 3
2.6
2.4
2.2
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
25.6

Figure 6.

MS/MS: 400 > 217.1951

25.8

26

26.2

26.4

26.6
26.8
27
Counts versus Acquisition time (min)

27.2

27.4

27.6

27.8

28

Comparison of extracted ion chromatogram at accurate mass (400.4064 ± 10 ppm) obtained in Full Scan mode (top) with the Extracted Product Ion(400 > 217.1951 ± 10 ppm) obtained in MS/MS mode (bottom) for the detection of steranes.

8Conclusions
The Agilent 7200 Q-TOF instrument allows the analysis of awide range of biomarkers in crude oil without the need forpre-fractionation. The diluted crude oil is directly analyzed
and biomarkers such as dibenzothiophenes and hopanes
could be selectively monitored by using extracted ion chromatograms at exact masses and using a narrow extraction
window.
The 7200 Q-TOF system operated in MS/MS mode also
allowed the selective detection of traces of steranes.
In summary, the Agilent 7200 GC/Q-TOF system can be efficiently utilized in targeted and untargeted biomarker analysis
in petroleum characterization.

References
1.

Z. Wang and M. Fingas, Developments in the analysis ofpetroleum hydrocarbons in oils, petroleum products andoil-spill-related environmental samples by gas chromatography, J. Chromatogr. A 774 (1997) 51-78.

2.

Z. Wang, M. Fingas, C. Yang and B. Hollebone, Biomarker
Fingerprinting: Application and Limitation for Correlationand Source Identification of Oils and Petroleum Products,
Prep. Pap.-Am. Chem. Soc., Div. Fuel Chem. 49 (1) (2004)
331-334.

For More Information
These data represent typical results. For more information on
our products and services, visit our Web site atwww.agilent.com/chem.

9

www.agilent.com/chem
Agilent shall not be liable for errors contained herein or for incidental or consequential
damages in connection with the furnishing, performance, or use of this material.
Information, descriptions, and specifications in this publication are subject to change
without notice.

© Agilent Technologies, Inc., 2011
Printed in the USA
December 2, 20115990-9477EN

Application Note
Environmental

A New Approach to the Analysis
of Chlorinated Paraffins by Gas
Chromatography Quadrupole
Time‑of-Flight Mass Spectrometry
Authors

Abstract

Wei Gao, Jing Wu,
Yawei Wang, and Guibin Jiang
State Key Laboratory ofEnvironmental Chemistry and
Ecotoxicology,
Research Center for
Eco‑Environmental Sciences,
Chinese Academy of
Sciences,
Beijing, China

Chlorinated parafﬁns (CPs) are industrial products produced and used in bulk
for various purposes. However, the analysis of CPs is challenging, as they arecomplex mixtures of compounds and isomers. This study develops an analyticalmethod for the analysis of short-chain CPs (SCCPs) and medium-chain CPs(MCCPs) using gas chromatography coupled with quadrupole time-of-ﬂight
high‑resolution mass spectrometry operated in negative chemical ionization mode
(GC‑NCI‑Q‑TOF‑HRMS). The linear relationship between chlorination and the CP
total response factors was applied to quantify the CP content and the congener
group distribution patterns. In a single injection, 24 SCCP formula groups and
24 MCCP formula groups were quantiﬁed. Extraction of accurate masses using
TOF-HRMS allowed the SCCPs and MCCPs to be distinguished, with interference
from other chemicals (for example, PCBs) being effectively avoided. The SCCP
and MCCP detection limits were 24–81 ng/mL and 27–170 ng/mL, respectively.
Comparison of the results with those obtained through gas chromatography
coupled with low-resolution mass spectrometry operated under the same
ionization mode (GC-NCI-LRMS) indicated that the developed technique was amore accurate and convenient method for the analysis of CPs in samples from a
range of matrices.

Wenwen Wang
Agilent Technologies, Inc.
Beijing, China

Introduction
Chlorinated paraffins (CPs), also known
as polychlorinated n-alkanes, have been
widely used for decades in commercial
products1,2,3. The commercial CP
mixtures can be divided into three
categories:•

Short‑chain chlorinated parafﬁns(SCCPs) C10–C13•

Medium-chain chlorinated parafﬁns(MCCPs) C14–C17•

Long-chain chlorinated parafﬁns(LCCPs) C >17.

Among these, SCCPs have drawn
signiﬁcant attention due to their hightoxicity2; however, as MCCPs and SCCPs
coexist in the environment, and MCCPs
can be transformed into SCCPs throughenvironmental processes such as
combustion, the issue of MCCP analysis
should also be addressed.
The quantiﬁcation of CPs inenvironmental samples is challenging4
due to the complexity of the industrial
mixtures and self-interference among theCPs. A number of different methods havebeen developed for the determinationof SCCPs and MCCPs in a range ofenvironmental matrices5-9. However,these methods encounter several
challenges such as high cost and the riskof interference between other chlorinatedpollutants and CPs with the same
nominal mass. Interference related tomass overlap between SCCP and MCCP
congeners must also be addressed, andfragmentation patterns should be studied
to allow more accurate quantiﬁcationof CPs. With these challenges in mind,
this Application Note describes a
published study on the developmentof a novel analytical approach basedon the GC-NCI-Q‑TOF-HRMS system
to simultaneously analyze SCCPs
and MCCPs in a single injection10.

2

High‑resolution TOF scan mode wasused to directly quantify SCCPs, andavoid possible interference by MCCPs
in environmental samples. Twenty‑four
different SCCP formula groups (C10–C13
with 5–10 chlorine atoms) and 24 MCCP
formula groups (C14–C17 with 5–10
chlorine atoms) were analyzed byextracting accurate masses. CPs bearing
fewer chlorine atoms and shorter chain
lengths were also studied. Samples froma range of environmental matrices wereanalyzed using the developed method,
proving that it is a more accurate andconvenient method for the analysis of
CPs in environmental samples.

Experimental
Reagents and Standards
Pesticide analytical grade solvents werepurchased from J.T. Baker (Phillipsburg,
NJ, USA). Solutions of the SCCP mixtures(100 ng/µL, C10–C13 with 51 %, 55.5 %,and 63 % chlorination, 100 % purity) and
MCCP mixtures (100 ng/µL, C14–C17
with 42 %, 52 %, and 57 % chlorination,
100 % purity) in cyclohexane andε-hexachlorocyclohexane (ε-HCH,
solution in cyclohexane, 10 ng/µL,
99.9 % purity) were purchased from
Dr. Ehrenstorfer GmbH (Augsburg,
Germany). 1,5,5,6,6,10-Hexachlorodecane(13C10 -,100 ng/µL, solution in
cyclohexane, ≥98 % purity) and
1,5,5,6,6,10-hexachlorodecane(unlabeled, 100 ng/µL in cyclohexane,≥98 % purity) were purchased from
Cambridge Isotope Laboratories(Andover, USA).

Instrument
GC Conditions
GC System

Agilent 7890B, coupled with a CTC autosampler;

ColumnAgilent HP-5MS UI, 30 m × 0.25 mm, 0.25 µm (p/n 19091S-433 UI)

Carrier gas

HeliumOven temperature program

100 °C hold 1 minute, at 5 °C/min to 160 °C hold 2 minutes,
at 30 °C/min to 310 °C hold 10 minutes

Flow rate

1.0 mL/min

Inlet temperature

280 ℃

Injection volume

2 μL

Injection mode

Splitless, purge on after 1.5 minutes

Transfer line temperature

280 °CQ-TOF MS Conditions
MS System

Agilent 7200 GC-Q-TOF

Ionization mode

Negative Chemical Ionization (NCI)

Source temperature

150 °C

Quadrupole temperature

150 °C

Mass range

50 to 600 m/z

Spectral acquisition rate

5 Hz, collecting both in centroid and profile modes

Acquisition mode

4 GHz high resolution

Sample Preparation
To test the performance of theNCI‑TOF‑HRMS method, samples
from several environmental matrices
were analyzed for both SCCPs
and MCCPs. Air samples wereobtained using a passive air sampler(Xpress‑Application Developer, XAD).
The industrial CP products werekindly provided by manufacturers.
Food samples were purchased fromseveral well-known fast food outlets.
Sample pretreatment was based on
the previously reported method11,12
with some minor modiﬁcations. Brieﬂy,frozen dried samples (1 g) were mixed
with diatomaceous earth (5 g), andspiked with the 13C10-1,5,5,6,6,10hexachlorodecane (10 ng) as surrogate
internal standard and quantitativeinternal standard prior to accelerated
solvent extraction (ASE). The extract
was concentrated to approximately
1 mL by rotary evaporation. The extract
was then cleaned and fractionatedon a 1.5 cm silica-Florisil composite
column packed with Florisil (3 g),neutral silica gel (2 g), acidic silica gel(5 g, 30 %), and anhydrous sodium
sulfate (4 g) (packed from bottom totop). The column was conditioned
with n-hexane (50 mL), and the sample
was eluted with n-hexane (40 mL)(fraction 1 contained polychlorinatedbiphenyls and toxaphenes), followedby dichloromethane (50 mL) andn-hexane (50 mL) (fraction 2 contained
CPs and HCHs). The second fraction
was concentrated to approximately
2 mL by rotary evaporation, and further
concentrated to close to dryness undera gentle stream of N2. The fraction
was then reconstituted in cyclohexane(200 µL). Prior to MS analysis, a ε-HCH(10 ng) was added as injection internal
standard to determine the sample
recoveries. Instrumental blanks werecomposed of pure cyclohexane. No CPs
were observed following injection of theblanks.

Results and Discussion
Quantification Method Workflow andAuto-Integration Procedure
Figure 1 describes the workflow for
the chlorination response factor-based
quantification method.
In the NCI-LRMS method, manual
integration was traditionally applied tocompare the peak shapes and retention
times with the reference standards. In
the NCI-TOF-MS method, the observed
extracted ion chromatography (EIC)
peak was comparable to that of thestandard, as high-resolution MS removed
interference from the matrix. Autointegration was applied using Agilent
MassHunter Quantitative Analysis B.07.

The accurate masses of the SCCPs and
MCCPs and quantitative and qualitativeions, along with their retention times(Table 1), were added to the method.
New batch files were built, and the data
files were imported. The integration
results could directly transfer tocustomer’s homemade excel table tocalculate the subsequent results listed
in Figure 1. The quantitative method,
based on the linearity of the response
factor and chlorination, compensatedfor the difference in response factors
between the reference CP mixtures andthe real samples8. Figure 2 shows thelinear relationship between the response
factor (RF: the ratio of internal standard
adjustment response to the CP content)
and calculated chlorination (%) for
MCCPs and SCCPs.

Extracting [M-Cl]– ions from TOF scan data

ISTD calibrated peak area

Adjusting response according to chlorine atom and abundance

Calculating response contribution of each congener

Calculating chlorination contribution of each congener

Quantification according to the chlorination of the sample
Figure 1. The quantiﬁcation method workﬂow for CPs. For detailed
information about the quantification method please see reference 10.

3

Table 1. Accurate mass of quantitative and qualitative [M-Cl]– ions for SCCPs and MCCPs, average molecular mass, retention time,and limit of detection of each single formula group (continued next page).
SCCP and MCCP
congeners (n, z)

Quantitative ionsm/z [M-Cl]–

Qualitative ions

Abundancem/z [M-Cl]–

Abundance

Average molecularmass

Retention time(min)

LOD (ng/mL)

314.5

9.5–1411.8

SCCP
C10H17Cl5

279.0006

37.60%

277.0009

29.40%

C10H16Cl6

312.9671

35.60%

314.9641

22.70%

349

11–13.5

7.5

C10H15Cl7

346.9281

32.30%

348.9251

25.90%

383.5

11.5–14

5.2

C10H14Cl8

380.8891

28.60%

382.8862

27.50%

418

12.5–14.5

4.78

C10H14Cl9

416.8472

27.80%

414.8501

24.80%

452.5

12.5–14.5

3.2

C10H12Cl10

450.8082

27.10%

448.811221.20%

487

11.5–16

1.1

C11H19Cl5

293.0217

37.20%

291.024629.10%

328.5

10 –14

9.05

C11H18Cl6

326.9437

35.20%

328.9798

22.50%

363

11–14

6.5

C11H17Cl7

360.9437

32.00%

362.9408

25.60%

397.5

12–14.5

1.6

C11H16Cl8

394.9048

28.30%

396.9018

27.20%

432

12–14.5

0.75

C11H15Cl9

430.8628

27.50%

428.8658

24.50%

466.5

12.5–14.5

0.75

C11H14Cl10

464.8239

26.70%

462.8268

20.90%

501

13.5–15.5

0.75

C12H20Cl5

307.0373

36.80%

305.0403

28.70%

342.5

11–14

5.55

C12H19Cl6

340.9984

34.80%

342.9954

22.30%

377

11.5–14

5.15

C12H18Cl7

374.9594

31.70%

376.9564

25.30%

411.5

12.4–14.6

1.45

C12H17Cl8

408.920428.00%

410.9175

26.90%

446

12.5–15

1.2

C12H16Cl9

444.8785

27.10%

442.8814

24.20%

480.5

13–15

1

C12H15Cl10

478.8395

26.40%

476.8425

20.70%

515

13.5–161

C13H22Cl5

321.05336.30%

319.0059

28.40%

356.5

11.5–14.5

10

C13H21Cl6

355.012334.40%

357.0111

22.00%

391

12.2–15

8.7

C13H20Cl7

388.975

31.30%

390.9721

25.00%

425.5

12.5–14.5

3.5

C13H19Cl8

422.9361

27.70%

424.9331

26.60%

460

13–15.52

C13H18Cl9

458.8941

26.80%

456.8971

24.00%

494.5

12.5–172

C13H17Cl10

492.8552

26.10%

490.8581

20.40%

529

14–17

1.75

4

Table 1. Accurate mass of quantitative and qualitative [M-Cl]– ions for SCCPs and MCCPs, average molecular mass, retention time,and limit of detection of each single formula group.
SCCP and MCCP
congeners (n, z)

Quantitative ionsm/z [M-Cl]–

Qualitative ions

Abundancem/z [M-Cl]–

Abundance

Average molecularmass

Retention time(min)

LOD (ng/mL)

370.5

12.2–14.2

9.3

MCCP
C14H25Cl5

335.0686

37.60%

333.0716

29.40%

C14H24Cl6

369.0697

35.60%

371.0267

22.70%

405

12.4–14.6

2.6

C14H23Cl7

402.9907

32.30%

404.9877

25.90%

439.5

12.8–15.2

5.5

C14H22Cl8

436.9517

28.60%

438.9488

27.50%

474

13.6–15.8

7.5

C14H21Cl9

472.9098

27.80%

470.9127

24.80%

508.5

14–16.8

3.5

C14H20Cl10

506.8708

27.10%

504.8738

21.20%

543

15–18

3.1

C15H27Cl5

349.0843

37.20%

347.0872

29.10%

384.5

12.2–14.4

7.7

C15H26Cl6

383.0453

35.20%

385.0424

22.50%

419

12.5–15.5

10

C15H25Cl7

417.0063

32.00%

419.0034

25.60%

453.5

13.8–15.2

38

C15H24Cl8

450.9674

28.30%

452.9644

27.20%

488

13.5–16.8

5.6

C15H23Cl9

486.9254

27.50%

484.9284

24.50%

522.5

14.6–18

4.6

C15H22Cl10

520.8865

26.70%

518.8894

20.90%

557

15.5–19.5

2.1

C16H29Cl5

363.0999

36.80%

361.1029

28.70%

398.5

12.5–15.5

9.6

C16H28Cl6

397.06134.80%

399.058

22.30%

433

13.5–15.5

11.7

C16H27Cl7

431.02231.70%

433.01925.30%

467.5

13.8–15.8

7.9

C16H26Cl8

464.983

28.00%

466.9801

26.90%

502

14.4–17.4

2.3

C16H25Cl9

500.9411

27.10%

502.9381

24.20%

536.5

15.5–19.5

1.6

C16H24Cl10

534.9021

26.40%

532.9051

20.70%

571

16.5–210.9

C17H31Cl5

377.1156

36.30%

375.1185

28.40%

412.5

12.5–15

8.6

C17H30Cl6

411.0766

34.40%

413.0737

22.00%

447

13.4–15.2

9.3

C17H29Cl7

445.0376

31.30%

447.0347

25.00%

481.5

13–17.5

2.7

C17H28Cl8

478.9987

27.70%

480.9957

26.60%

516

14.5–191

C17H27Cl9

514.9567

26.80%

512.9597

24.00%

550.5

16.5–20.5

1.2

C17H26Cl10

548.9178

26.10%

546.9207

20.40%

585

18–23

1.3

5

Limit of Detection (LOD) andLinearity Range

0.10

The instrumental LOD was determinedas the standard deviation of the signal
intensities from the ﬁve replicate
injections multiplied by Student’s T-value
at a 95 % conﬁdence level. In real
samples, detection of a congener group
was deﬁned as both m/z values of thequantitative and qualitative ions being
detected above their respective LODs,
and where the LOD of the congener
group was equal to the LOD of the leastsensitive of the two monitored m/z
values. The LOD for the SCCPs and
MCCPs was deﬁned as detection of themost abundant congener group. Results
showed that the LOD of the MCCPs
was in the range of 27–170 ng/mL,
while that of the SCCPs was in therange of 24–81 ng/mL. Table 1 provides
the LOD of each formula group. The
linearity of the NCI‑Q‑TOF‑HRMS
method was determined by fitting theintensities obtained from the standard

0.08

×103
35

0.09

0.07

MCCP

y = 0.019x – 1.13
R2 = 0.97

0.06

y = 0.02x – 1.11
R2 = 0.98

0.05
RF

RF

0.05
0.04

0.03
0.02

0.02

0.01

0.01
0
60

61
62
63
64
Calculated chlorination (%)

solutions of 55.5 % Cl SCCP, 52 % Cl
MCCP, and 57 % Cl MCCP mixtures
against their concentrations ranging
from 0.25 to 100 ng/ µL using weighted
linear regression. Figure 3 shows thecorresponding fitting curves. It was

×103
50

B

50

45

y = 553x – 1691
R2 = 0.99

40

57
59
61
63
Calculated chlorination (%)

C
y = 421x – 927
R2 = 1.00

30

30

25
20

20

10
5

10

0

0

0

20

40

60
ng/µL

80

100

120

15
10
5
0

20

40

60
ng/µL

80

100

120

0

0

20

40

60
ng/µL

80

100

120

Figure 3. A) Linearity of 55.5 % chlorinated SCCP mixtures (0.25–100 ng/ µL). B) Linearity of 52 % chlorinated MCCP mixtures (0.25–100 ng/ µL).
C) Linearity of 57 % chlorinated MCCP mixtures (0.25–100 ng/ µL).

665

found that the linearity ranges for both
SCCP and MCCP can reach three ordersof magnitude, which are higher than thatof the NCI-LRMS method8. This relatively
good linearity performance for the CPs
was due to no isomer reaching its upper
limit, even at high total concentrations.

MCCP 57 % Cl

35

ng

ng

0
55

40

20
ng

65

Figure 2. Linear relationship between the response factor (RF: the ratio of internal standard adjustment
response and the CPs content) and calculated chlorination (%) for MCCPs and SCCPs. A) Standard curveof SCCPs at 10 ng/µL (different chlorination obtained by mixing 51.5 % Cl, 55.5 % Cl, and 63 % Cl SCCP
standards). B) Standard curve of MCCPs at 10 ng/µL (different chlorination obtained by mixing 42 % Cl,
52 % Cl, and 57 % Cl MCCP standards).

MCCP 52 % Cl

15

0.04

0.03

60

y = 273x – 398
R2 = 1.00

25

0.07

×103

A

B

SCCP

0.06

SCCP 55.5 % Cl

30

0.08

A

Accuracy and Repeatability

Table 2. Accuracy and repeatability of the NCI-TOF-HRMS method.

Accuracy was calculated as theratio between the average measured
concentration (n = 5) and the reference
SCCP and MCCP mixture standards
at different chlorination percent(51.5 % Cl SCCP, 55.5 % Cl SCCP,
63 % Cl SCCP, 52 % Cl MCCP, and
57 % Cl MCCP). Table 2 shows theresults.
With the NCI-TOF-MS method, therelative accuracies for SCCPs and
MCCPs can be acquired within therange of 86–124 % and 114–129 %,respectively. When using the binary
mixture standards of SCCP and MCCP,
larger positive bias was observed thanthat for the single mixture standard.
Repeatability can be determined by thestandard deviation of repeated injections(n = 18, spiked at 1, 10, and 100 ng/L ofboth 55 % Cl SCCP and 52 % Cl MCCP)
over a single day (intra-day) and acrossseveral days (inter-day). The relative
standard deviations (RSDs) of SCCP
for the inter-day injections obtained by
NCI-TOF-MS at the three concentrationlevels were 2.55 %, 1.95 %, and 3.58 %,respectively. For the MCCP, thecorresponding RSDs were 12.3 %, 7.37 %,and 0.97 %, respectively.

Inﬂuence of Resolution in CP Analysis
The relationship between resolution anddeviation of mass (DM) is defined by
Equation 1.
Resolution =Reference Calculatedconc.
conc. (±error)(ng/µL)(ng/µL)

Performance
test

Binary mix

Accuracya

Reference Calculated
conc.
con. (±error)(ng/µL)(ng/µL)
Accuracya

SCCP Test
51 % Cl SCCP

5.00

4.30 (±0.41)

86 %55 % Cl SCCP

10.00

10.00 (±0.19)

100 %55 % Cl SCCP and
57 % Cl MCCP

10.00

12.43 (±4.6)

124 %

10.00

12.89 (±0.27)

129 %(1:1, v/v 20 ng/µL)

63 % Cl SCCP

10.00

12.05 (±0.14)

120 %MCCP Test
52 % Cl MCCP

10.00

12.13 (±0.89)

121 %

57 % Cl MCCP

10.00

11.36 (±0.71)

114 %

55 % Cl SCCP and
57 % Cl MCCP
(1:1, v/v 20 ng/µL)aAccuracy is deﬁned as the percentage ratio of the calculated concentration of CPs and the reference
concentration of CPs.

Equation 1 shows that the resolutionof a signal is related to the mass of thespecies. In this case, the majority of
CP target ions were in the m/z rangeof 300 to 500, where TOF resolutionsof 10,000–15,000 could theoreticallyyield mass accuracies of 5–10 ppm.
For the 96 quantitation and qualiﬁcation
fragments, a minimum resolution of
3,000 was required for separationof the two closest m/z values for
the C12H1635Cl737Cl2 (478.839 Da) andC17H2835Cl637Cl (478.9987 Da) fragments(Table 3). The ion source temperatureof 150 °C was selected to minimize
fragmentation patterns other than
[M−Cl]−5,11. Indeed, if [M−Cl]− could beconsidered the main fragmentation
pattern, the resolution requirement would
be 8,000 (C12H1635Cl737Cl2 = 478.839 Da,
and C17H2835Cl637Cl = 478.9987 Da). Thus,
the TOF-HRMS method applied in thisstudy with a resolution of 12,000–15,000
was suitable for resolving all congener
groups of SCCPs and MCCPs.

Table 3. Accurate masses of MCCP and SCCP formulation groups
that generated fragmentation ions with the same nominal mass, andthe D-value between the two ions.
Nominalmass

M
DM

Equation 1.

M is the m/z ratio of the fragment ions,
DM is the mass distance between two
adjacent peaks.
aFormulagroup

Accurate
mass

Formulagroup

Accuratemass

D-value(ppma)

417

C10 Cl9416.8472

C15 Cl7417.0063

382

451

C10 Cl10450.8082

C15 Cl8450.9674

353

431

C11 Cl9430.8628

C16 Cl7431.022369

465

C11 Cl10464.8239

C16 Cl8464.983342

445

C12 Cl9444.8785

C17 Cl7445.0376

358

479

C12 Cl10478.8395

C17 Cl8478.9987

333

Part per million

7

Furthermore, matrix interference wasfound to exist even after following
thorough sample pretreatment
procedures14. Under the NCI-LRMS
system, SIM combined the retention time
window to eliminate self-interference.
However, this approach did not yieldsatisfactory results (Figure 4: EIC at±0.5 amu), as baseline separation of
the components could not be achieved.
Figure 4 shows that interference fromthe matrix along with CP self-interference(for example, m/z 451 generated by
C10Cl10 and C15Cl8) could be avoided, toa large extent, with a mass tolerance of
50 ppm upon extracting the accuratemass. In Figure 4, MCCPs were treatedas interference, while SCCPs wereregarded as the targets.

73,172 ng/g dw for the food samples.
In addition, the SCCP concentrationin the XAD-based air samples ranged
from 0.04–29 ng/m3, and ﬁnally, for thetechnical products, the SCCP content
ranged from 54 to 1,651 ng in the CP-52
products at a concentration of 10 ppm.
The SCCP contents and chlorination
values obtained using the two different
MS methods were also compared(Figure 5).

Analysis of Environmental Samples
and Comparison Between Two
Methods

In the XAD-based air samples, theconcentrations obtained using the
NCI‑TOF-HRMS method were prevalently
lower than those obtained by the
NCI‑LRMS method, with the exceptionof two cases (an extremely low content(13 bz) and an extremely high content(14 dppl)). The SCCP concentrations
determined by NCI‑TOF‑HRMS differedfrom those obtained by NCI-LRMS byfactors of 0.19–0.92.

To assess improvements in thequality of the CP environmental
measurements13 (Figure 5), it isessential to compare the of results fromthe current HRMS method with the
LRMS method previously reported11,12.
The NCI‑TOF‑HRMS method wasevaluated to quantify SCCPs and
MCCPs in industrial products, food, and
XAD‑based air samples (Figure 5). The
SCCP concentration ranged from 70 to

For the industrial CP products, theconcentrations obtained using the
NCI-TOF-HRMS method were generally
higher than those obtained using theNCI-LRMS method, with the exception of

A ×106

B ×106CountsCountsConversely, the results obtained for the
food samples varied due to different
matrix effects. The SCCP concentrations
determined by NCI-TOF-HRMS differedfrom those obtained by NCI-LRMS byfactors of 0.16–2.55.

Counts0×102

EIC 451 ±0.5 amu

Interferences from matrix
Interferences from MCCP

4
2
6

7

8

9 10 11 12 13 14 15 16 17
Acquisition time (min)Counts4 EIC 450.8082 ±50 ppm
2

To further investigate the reasons for thedifferences in results obtained using thetwo methods, individual formula groupcontents of the SCCPs obtained using
both the NCI-TOF-HRMS method andthe NCI-LRMS method were compared(Figure 6). This comparison showed that,in air, SCCPs containing fewer chlorine
atoms and shorter chain lengths werepredominant, whereas the reverse wastrue for the technical products. At highresolutions, the obtained content of
CPs containing fewer chlorine atoms
was lower. Therefore, for the lighter
components found in XAD-based air
samples, the content determined by
NCI-TOF-HRMS was higher, while for theheavier components found in technicalproducts, the content determined by
NCI-TOF-HRMS was lower (Figure 6).
However, the differences in the absoluteamounts obtained did not represent
significant deviations from the true
values. As discussed above, CPs withvarying chlorine contents exhibited
various response patterns related todifferent instrumental conditions, whichmainly resulted from the varying degrees
of chlorination.

TIC
4 Food sample

2

0
×104

EIC 416.8472 ±50 ppm

2

0
×104
CountsCounts

6 TIC
4 Air sample
2
0
×101

CP8, which had a very low SCCP content.
The SCCP concentrations measuredby NCI-TOF-HRMS differed from thosemeasured using NCI-LRMS by factors of
3.79–6.05.

Interferences from matrix

EIC 417 ±0.5 amu
2Interferences from MCCP

0
8

9

10

11 12 13 14 15
Acquisition time (min)16

17

Figure 4. Elimination of matrix effects in samples by accurate mass extraction, presented by EIC comparison. A) Total
ion chromatogram (TIC) of an air sample and the corresponding EIC extraction (C10Cl10) at different mass tolerances(50 ppm and 0.5 amu); B) TIC of a food sample and the corresponding EIC extraction (C10Cl9) at different mass tolerances(50 ppm and 0.5 amu).

8

65

A

100,000

Food

64

B

Food

NCI-LRMS
NCI-TOF-HRMS

10,000ng/g dw

63
%

62
61

1,000
100

60
10

59

68

10,000

C

66

2

1

5

Po
ta
to

Ra
zo
r

To
m
at
o

Pe
ar
3

3
Ca
rp

5
To
m
at
o

2

1
Ra
zo
r

Industrial CPs products

To
m
at
o

2
Po
ta
to

3
Ca
rp

70

Pe
ar
3

2

1

To
m
at
o

58

Industrial CPs products

D

1,000
ng

%

64
62

100

60
58

10

56

63

100,000

5

7

CP

CP

8

4
CP

CP

3
CP

7
CP

62

2

5
CP

Air-XAD

E

CP

4
CP

8

3
CP

CP

2

1
CP

54

Air-XADF

10,000

61
ng

14
xp
11
3
14
xp
11
4

l
14
sd

14
xp
11
3
14
xp
11
4

14
dp
p

14
dp
p

1
l
14
sd

57
13
pl

10

13
41
96
13
dp
bz
13
xp
31
8
13
bz
13
nc

58

13
pl

100

59

13
41
96
13
dp
bz
13
xp
31
8
13
bz
13
nc

%

1,000
60

Figure 5. Comparison of calculated chlorination and content of SCCPs obtained with the NCI-TOF-HRMS method (blue bars) and the NCI-LRMS method (greenbars) for (A) calculated chlorination comparison in food samples, (B) content comparison in food samples, (C) calculated chlorination comparison in industrial CP
products, (D) content comparison in industrial CPs products, (E) calculated chlorination comparison in XAD-based air samples, and (F) content comparison in
XAD-based air samples.

40
35

A Industrial products

LRMS

35

TOF

30

30

LRMS
TOF

25

25

20

20

%

%

B Air-XAD

15

15
10

10

0

-5

-5

C10Cl5
C10Cl6
C10Cl7
C10Cl8
C10Cl9
C10Cl10
C11Cl5
C11Cl6
C11Cl7
C11Cl8
C11Cl9
C11Cl10
C13Cl5
C12Cl6
C12Cl7
C12Cl8
C12Cl9
C13Cl10
C13Cl5
C13Cl6
C13Cl7
C13Cl8
C13Cl9
C13Cl10

0

C10Cl5
C10Cl6
C10Cl7
C10Cl8
C10Cl9
C10Cl10
C11Cl5
C11Cl6
C11Cl7
C11Cl8
C11Cl9
C11Cl10
C13Cl5
C12Cl6
C12Cl7
C12Cl8
C12Cl9
C13Cl10
C13Cl5
C13Cl6
C13Cl7
C13Cl8
C13Cl9
C13Cl105

5Figure 6. Comparison of SCCP individual congener contents obtained using the NCI-TOF-HRMS method (green bars) and the NCI-LRMS method (red bars)for (A) congener group distribution of SCCPs in industrial products, and (B) congener group distribution of SCCPs in XAD-based air samples.
9

The calculated chlorine content obtainedusing the NCI-TOF-HRMS method
were generally higher than the degrees
of chlorination calculated using the
NCI‑LRMS method, with the exceptionof industrial CP products and two foodsamples (potato 2 and razor 1). Thisvariation could be accounted for the lowcontent of CPs bearing fewer chlorine
atoms (CPs that generate ions withm/z ~300), as determined under a highresolution (in the SIM of the LRMS,
interference occurred at m/z ~300).
In this study, analyses of MCCPs
were conducted using only the
NCI‑Q‑TOF‑HRMS system. Due toa shortage of available data in theliterature, no inter-lab comparison
results were available for MCCPs.
Results from this study showed that the
MCCP concentration in air was lower
compared to that in other matrices,with the contents ranging from 0.04 to
0.89 ng/m3 obtained for the XAD-based
samples. In the food samples, the MCCP
levels were between 603 ng/g and
7,478 ng/g.
For the industrial products, theconcentration of LCPs (SCCPs + MCCPs)
were in the range of 3,796–6,235 ng
in six CP-52 products (CP2, CP3, CP4,
CP5, CP7, and CP8) of 10 ng/µL (inwhich the total amount of LCPs should
be 2,000 ng), indicating that the results

10

were overestimated. It is possible thatthe calculated degree of chlorinationof the industrial products were at thelow end of the calibration curve ofchlorine content versus MS response.
The degree of chlorination was inversely
correlated with the quantiﬁcation
results, and the lower chlorine content
of the industrial products relative to theenvironmental matrices might result inthe overestimation of CP concentrations.
The results implied that more speciﬁc
reference standards with a wider
chlorination range should be synthetized
to build more accurate quantiﬁed andqualiﬁed CP methods for different
matrices.

Time Efﬁciency and Suitability for
Routine Analysis
Unlike the earlier LRMS method11,12 that
required four separate runs to acquire
all necessary SIM ions for identiﬁcationand quantiﬁcation, the new HRMS
approach only required one injection.
The higher selectivity afforded by the
HRMS approach allowed effective
use of automatic peak integration
without signiﬁcant interference
instead of the time‑consuming manual
integration required for LRMS data. This
combination reduced turnaround timeon samples from a few months to a few
days.

Conclusions
The novel GC-Q-TOF-MS method offers
a number of beneﬁts over established
GC/NCI-LRMS methods for the analysis
of CPs in environmental samples. Thismethod was especially efﬁcient in thesimultaneous analysis of SCCPs and
MCCPs in complex environmental
samples, and was efficient in eliminating
CP self-interference by accurate mass
extraction. The results obtained for
different environmental samples
showed that the high-resolution TOF‑MS
method was capable of reducing
interferences from different matrices.
In addition, the GC-Q-TOF-MS method
shows a comparable linear dynamic
range and detection limits to previous
methods, along with improved accuracy.
Moreover, this method is suitable forhigh-throughput analyses of large
sets of samples due to its efﬁciency inboth analysis time and quantiﬁcation
processes. Further application ofthis GC-Q-TOF-MS method should beconsidered to achieve more accurate
analyses of CPs in different matrices.

References
1.

Muir, G. A.; Stern, G. T.; Tomy, J.;
Paasivirta (Eds.) The Handbook ofEnvironmental Chemistry, in: D.C.G.,
Springer: New York, 2000.

2.

European Commission, Directive
2000/60/EC of the European
Parliament and of the Council of
23 2000 establishing a framework
for Community action in the ﬁeld ofwater policy. Ofﬁcial J. Eur. Commun.
2000.

3.

4.

5.

De Boer, J. The Handbook ofEnvironmental Chemistry, Springer:
Berlin Heidelberg, 2016, 2010.

6.

Reth, M. Oehme, M. Limitations of
low resolution mass spectrometry
in the electron capture negative
ionization mode for the analysis ofshort- and medium-chain chlorinatedparafﬁns. Anal. Bioanal. Chem. 2004,
378, 1741–1747.

7.

Korytar, P.; et al. Characterizationof polychlorinated n-alkanes using
comprehensive two-dimensional gas
chromatography–electron-capture
negative ionization time-of-ﬂight
mass spectrometry. J. Chromatogr. A
2005, 1086, 71–82.

8.

Van Mourik, L. M.; et al. Recent
developments in capabilities foranalysing chlorinated parafﬁns inenvironmental matrices: a review.
Chemosphere 2015, 136, 259–272.
Tomy, G. T.; et al. Quantifying C10–C13
polychloroalkanes in environmental
samples by high-resolution gas
chromatography/electron capture
negative ion high-resolution mass
spectrometry. Anal. Chem. 1997, 69,
2762–2771.

9.

Reth, M.; Zencak, Z.; Oehme, M.
New quantiﬁcation procedure forthe analysis of chlorinated parafﬁns
using electron capture negative
ionization mass spectrometry.
J. Chromatogr. A 2005, 1081,
225–231.
Steinberg, S. M.; Emerson, D. W.
On‑line dechlorination–hydrogenation of chlorinated parafﬁnmixtures using GC and GC/MS.
Environ. Monit. Assess. 2012, 184,
2119–2131.

11. Zeng, L. X.; et al. Spatial andvertical distribution of short chain
chlorinated parafﬁns in soils fromwastewater irrigated farmlands.
Environ. Sci. Technol. 2011, 45,
2100–2106.
12. Wang, T.; et al. Summer-winterconcentrations and gas-particle
partitioning of short chain
chlorinated parafﬁns in theatmosphere of an urban setting.
Environ. Pollut. 2012, 171, 38–45.
13. Sverko, E.; et al. Improving the qualityof environmental measurements on
short chain chlorinated parafﬁns to
support global regulatory efforts.
Environ. Sci. Technol. 2012, 46,4697–4698.
14. Chen, L. G.; et al. Sample
pretreatment optimization for
the analysis of short chain
chlorinated parafﬁns in soil with gas
chromatography–electron capture
negative ion-mass spectrometry.
J. Chromatogr. A 2013, 1274, 36–43.

10. Gao, W.; et al. Quantiﬁcation of shortand medium-chain chlorinatedparafﬁns in environmental samples
by gas chromatography quadrupole
time-of-ﬂight mass spectrometry.
J. Chromatogr. A 2016, 1452,
98–106.

11

www.agilent.com/chem
This information is subject to change without notice.
© Agilent Technologies, Inc. 2017
Printed in the USA, October 9, 20175991-8583EN

Environmental Exposure and Toxicity
Profiling in Biological Matrices

Confident nontargeted screening of unknowns in biological matrices
Concentration and chemical toxicity determine the physiological response to acute or
chronic environmental contaminant exposure. Effects-directed analysis helps you identify
the impact that such exposure has on developing biota and humans. To identify the most
relevant chemical contaminants, you must determine parent contaminants, along withdegradation and transformation products in water and soil. You also need to identify in vivo
concentrations of parent and metabolites. Success depends upon nontargeted screening
using cellular assays and advanced MS technology.
Count on Agilent to support your research into emerging contaminants in biologicalmatrices. Our sample preparation and analytical tools provide speed to results for suspect
and nontargeted screening—eliminating false positives without compromising data quality
and spectral resolution. In addition, our cellular analysis portfolio lets you perform sensitive,
robust identification of modifications to cellular functions when exposed to contaminants.
Back to Table of Contents
Back to Introductionwww.agilent.com/chem/environmental

164

Application Note
Environmental

Analysis of Per- and Polyfluoroalkyl
Substances (PFASs) in Biological
Fluid Using a Novel Lipid Removing
Sorbent and LC-MS/MS
Authors

Abstract

Tarun Anumol, Joan Stevens,and Xiaomi Xu
Agilent Technologies, Inc.

Efficient sample preparation prior to LC-MS/MS analysis of per- and polyfluoroalkyl
substances (PFASs) is an important consideration for environmental contamination
research laboratories performing multiresidue analysis. Phospholipids (PPLs) havebeen identified as a major cause of matrix effects in the LC-MS/MS analysis ofplasma samples. This Application Note describes plasma sample preparation and
LC-MS/MS analysis of PFASs using in-well PPT followed by PPL removal using
the Agilent Captiva EMR—Lipid cartridge. Captiva EMR—Lipid cartridges produced
cleaner eluents, with removal of over 99 % of unwanted PPLs from the plasma
matrix, and over 75 % recovery of target analytes, with RSDs <14 %. Analysis of
PFASs at 5 ng/mL yielded ideal peak shapes with good signal‑to‑noise (S/N).
Calibration curves for all PFASs from 0.1–50 ng/mL were linear, with an R2 >0.992.

Introduction
Per- and polyfluoroalkyl substances (PFASs) are man-madecompounds widely used as surfactants, fire-retardants,
waterproofing, and nonstick and nonstain agents. Theirunique properties also make them persistent and ubiquitousin the environment and in animals. Research suggests that
PFASs can cause reproductive and developmental problems
such as liver, kidney, and immune effects, tumors, andchanges in cholesterol. When PFASs are ingested by drinking
or eating, they are readily absorbed, but slowly cleared, andcan accumulate in animal tissue. Studies have shown that
PFASs with carbon chains longer than seven carry the mostrisk for bioaccumulation1.
Efficient sample preparation prior to LC-MS/MS analysis of
PFASs is an important consideration for multiresidue analysis.
Sample preparation is used to reduce system contamination,
improve data integrity and method selectivity, and to enhance
analytical sensitivity. Two of the major interferences foundin plasma are proteins and phospholipids (PPLs). PPLs
have been identified as a major cause of matrix effects in
LC-MS/MS bioanalyses due to competition for space on thesurface of droplets formed during electrospray ionization(ESI)2.
Common sample preparation techniques for plasma, serum,
and whole blood in research laboratories include protein
precipitation (PPT), solid phase extraction (SPE), liquid-liquid
extraction (LLE), and supported liquid extraction (SLE). Eachtechnique has advantages and disadvantages in terms ofspeed, cost, and quality of data generated. For example,
PPT, LLE, and SLE do not remove PPLs, and SPE is moretime‑consuming and complicated to perform. However,of these techniques, PPT is most widely used. Using PPT,
proteins are easily removed by adding an organic crashsolvent such as ACN or MeOH into bio-fluid samples in aprescribed ratio. As the proteins are denatured, they form aprecipitate that can be removed by filtration or centrifugation.
PPLs are not removed by PPT because they are soluble in theorganic crash solvent.
A sample preparation method that eliminates certain sample
preparation steps, including off-line PPT, centrifugation, anddilution, while allowing streamlined in-well PPT and PPL
removal, is highly desirable. This Application Note describes
an approach that relies on Agilent Captiva EMR—Lipid toremove interferences, particularly PPLs, without analyte loss,
in a simple pass-through format. The resulting extract iscleaner, reducing potential ion suppression, and column andmass spectrometer contamination.

2

Extraction of PFASs from plasma was performed using
in-well PPT followed by PPL removal using a Captiva
EMR—Lipid cartridge. Subsequent quantitative analysis was
performed using an Agilent 6495 Triple Quadrupole LC/MS
system. Efficiency of PPLs removal was evaluated. Method
reproducibility and recovery for the PFASs evaluated werealso determined.

Experimental
Reagents and Chemicals
Table 1 lists the PFASs analyzed. All standards and internal
standards were purchased from Wellington Laboratories(Guelph, ON, Canada). LC-MS grade ammonium acetate
was purchased from Sigma-Aldrich. All solvents were LC-MS
grade or higher, and were obtained from Burdick and Jackson(Muskegon, MI, USA).
Table 1. PFASs and IS analyzed, with corresponding triple quadrupole MRM
acquisition parameters.
Precursor ion(m/z)

Product ion(m/z)

PFTrDA

663

618.7

8

3.16

PFDoA

613

268.7

20

2.92

PFDoA-13C2

614.9

570

5

2.92

N-EtFOSAA

584

526

20

3.40

10:2 FTA

577

463

6

2.51

N-MeFOSAA

570

511.8

24

3.23

N-MeFOSAA d3

573

515

24

3.23

PFUdA

563

519

8

2.70

PFDA

513

468.6

8

2.48

CompoundCollisionenergy (eV)Retention
time (min)

PFDA

513

218.7

16

2.48

PFDA-13C2

514.9

469.9

5

2.48

PFOS

498.9

99

50

2.61

PFOS

498.9

80

50

2.61

PFOS-13C4

502.9

80

50

2.61

8:2 FTA

477

393

14

2.12

PFNA

462.9

418.9

5

2.25

PFNA

462.9

169

17

2.25

PFOA

412.9

368.9

5

2.00

PFOA- C4

416.0

371.9

5

2.00

PFHxS

398.9

99

45

2.10

PFHxS-13C3

401.9

79.8

52

2.10

13

6:2 FTA

377

293

18

1.51

PFHpA

362.9

319

5

1.67

PFHxA

313

268.6

4

1.07

PFHxA-13C2

314.9

269.9

5

1.07

PFBS

298.9

98.9

29

1.15

PFPeA

263

218.7

0

0.63

PFBA

213

168.7

4

0.47

PFTeDA

712.9

668.5

8

3.33

Solutions
A combined standard working solution of PFASs was made at
10 ug/mL in methanol. The isotopically-labeled PFASs werecombined in a working solution at 10 ug/mL in methanol,and used as internal standard (IS). All working solutions
were stored in polypropylene vials with snap caps andpolypropylene-lined septa to prevent the PFASs from sticking
to the glass and to avoid contamination.

Calibration Standards and Quality Control Samples
Prespiked quality control (QC) samples were fortified withstandard working solution to the appropriate concentrations
in replicates of seven. The QC samples were low QC (LQC),
middle QC (MQC), and high QC (HQC), corresponding to 1, 5,
and 20 ng/mL in plasma, respectively. The IS was spiked at
10 ng/mL at each QC level.
Blank matrix after cleanup by Captiva EMR—Lipid was
post‑spiked with a corresponding working solution to yield 1,
10, and 20 ng/mL concentrations of PFASs. The IS was spiked
to a final concentration of 10 ng/mL, in replicates of five.
Matrix-matched calibration curves were prepared withthe standard working solution. Blank matrix after Captiva
EMR—Lipid was post-spiked to correspond to 0.1, 1, 5, 10, 25,
and 50 ng/mL in plasma. The IS was spiked at 10 ng/mL ateach calibration level.

Equipment and Instrumentation
Table 2 provides the list of the equipment and instrumentation
used to perform the analysis.

LC-MS/MS Analysis
An Agilent 1290 Infinity II LC System coupled to an
Agilent 6495 Triple Quadrupole LC/MS system was used
for the LC-MS/MS analysis. Tables 3 and 4 provide the LC
and MS conditions. The sample extracts (4 µL) were directly
injected into the LC system.
Table 1 provides the triple quadrupole dynamic multiple
reaction monitoring (DMRM) acquisition parameters for each
PFAS compound monitored. To evaluate PPL removal by
Captiva EMR—Lipid, 11 major PPL compound precursor ions,and the product ion fragment at m/z 184 were monitored, as
shown in Table 5.

Table 2. Equipment and instrumentation used for sample preparation andanalysis.
Component

Part number

Sample Preparation
Agilent Captiva EMR—Lipid, 1 mL cartridge

5190-1002

Agilent Vac Elut SPS 20 Manifold with collection rack for
13 × 100 mm test tubes

12234101

VWR 13 × 100 mm culture tubes 8 mL polypropylene
Eppendorf pipettes and repeater pipettor (VWR, NJ, USA)
Liquid Chromatography System
Agilent 1290 Infinity II LC System
Agilent ZORBAX Eclipse Plus 95Å C18, 4.6 × 50 mm, 3.5 µm(delay column)

959943-902

Agilent InfinityLab Poroshell 120 EC-C18, 2.1 × 50 mm, 2.7 µm

699775-902T

Agilent 1290 Infinity inline filter 0.3 µm

5067-6189

Crimp/snap-top polypropylene vials, 1.0 mL, 100/pk

5182-0567

Crimp/snap caps with polypropylene septa, 100/pk

5182-0542

Mass Spectrometry System
Agilent 6495 Triple Quadrupole LC/MS system with iFunnel
Technology
Agilent MassHunter Software (Ver. 08.00)

Table 3. LC conditions.
Parameter

Value

Flow rate

0.5 mL/min

Colum temperature

50 °C

Autosampler temperature

5 °C

Injection volume

4 µL

Mobile phaseA) 5 mM Ammonium acetate in waterB) Acetonitrile

Needle wash: Multiwash

S1) H2O
S2) H2O:ACN (50:50)
S3) ACN
10 seconds each wash

Gradient

Time (min)
0.0
0.5
3.5
4.5

Stop time

5.0 minutes

Post time

1.5 minutes

%B
30
30
90
100

Table 4. MS conditions.
Parameter

Value

Ionization mode

Negative ESI

Gas temperature

130 °CGas flow

15 L/min

Nebulizer

35 psiSheath gas heater

375 °C

Capillary voltage

2,000V

Vcharging

500

Delta electron multiplier voltage (EMV)

200

Polarity

Negative3

Table 5. Triple quadrupole MRM acquisition parameters for PPLs.

PPL Removal Evaluation, PPT Only1.

Add 400 µL of ACN (1 % FA) to a test tube.

302.

Add 100 µL of blank plasma, prespun.

184.4

30

3.

184.4

30

784.4

Vortex on a Heidolph Multi Reax at 800–1,000 rpm for
5 minutes.

184.4

30

760.4

184.4

30

4.

Centrifuge at 5,000 rpm for 5 minutes.

758.4

184.4

30

5.

704.4

184.4

30

Pipette the supernatant into a polypropylene autosampler
vial for LC-MS/MS analysis.

524.4

184.4

30

522.4

184.4

30

520.4

184.4

30

496.4

184.4

30

Precursor ion (m/z)

Product ion (m/z)

Collision energy (eV)

808.4

184.4

806.4
786.4

Agilent MassHunter Software (Ver. 08.00) was used for
instrument control, and for qualitative and quantitative dataprocessing and analysis. Reproducibility and recovery of themethod for PFASs were determined.

Sample Preparation Procedures
PFAS Extraction from Plasma

Results and Discussion
Unwanted Lipid Matrix Removal
The EMR—Lipid approach is simple and universally applicable
to reducing matrix effects and improving analyte recoveries
for the analysis of polar, midpolar, and nonpolar target
analytes, in research laboratories. The EMR—Lipid sorbent
selectively traps lipids by size exclusion and hydrophobic
interaction (Figure 1). Unbranched hydrocarbon chains (lipids)
enter the pores of the sorbent, but bulky analytes do not. Lipid
chains that enter the sorbent are then trapped by hydrophobic
interactions.

1.

Add 400 µL of ACN (1 % FA) to the Captiva EMR—Lipid
1 mL cartridge.

2.

Add 100 µL of spiked or blank human plasma, prespun.

3.

Perform in-well mixing.

4.

Pull a low vacuum of 2–4 psi for a controlled flow rate of
1 drop per 3–5 seconds.

5.

Collect the extract in polypropylene test tubes.

Analyte6.

Inject directly onto the LC-MS/MS system using
polypropylene autosampler vials.

Lipid

Because MeOH forms smaller precipitant particles than ACN,
ACN is recommended to maximize PPT and avoid gelation
prior to Captiva EMR—Lipid treatment. A ratio range of 1:3
to 1:5 (sample/solvent) is recommended. Plasma sample isadded after the crash solvent. Acid (formic acid) helps break
up proteins, and minimizes protein binding.
Preferably, active in-well mixing is done using wide-bore
pipette tips. The vacuum initiates flow through the Captiva
EMR—Lipid cartridge. A controlled flow rate of one drop per
3–5 seconds is recommended for optimal lipid removal. After
sample elution off the cartridge, higher vacuum is applied tomaximize sample recovery. Polypropylene collection tubes
and autosampler vials are highly recommended to prevent
PFAS loss due to sticking on glass surfaces.

4

Size exclusion: Unbranched hydrocarbon chains(lipids) enter the sorbent; bulky analytes do not.

EMR Sorbent

Sorbent chemistry: Lipid chains thatenter the sorbent are trapped byhydrophobic interactions.
Figure 1. EMR—Lipid mechanism: size exclusion and hydrophobic
interactions.

Chromatographic Performance

Though the PFAS structures shown in Figure 2 containa long straight carbon chain, the carbon is attached tofluorine atoms, which are sterically larger than unbranchedhydrocarbon chains. Therefore, they are sterically hinderedfrom entering into the pores of EMR—Lipid sorbent.
O
CF3

OH

OH

n = 5 PFHpA
n = 6 PFOA
n = 7 PFNAn = 8 PFDeA
n = 9 PFUA
n = 10 PFDoAn = 3 PFBuSn = 5 PFHxSn = 7 PFOS

(CF2)n

PPL Removal
PPLs are the main constituents of cell membranes
and the main class of compounds that causesignificant matrix effect3,4. Glycerophophocholines andlysophosphatidylcholines represent 70 % and 10 % of thetotal plasma PPLs, respectively5, and are the major source of
matrix effects. To determine the efficiency of PPL removalfrom plasma using Captiva EMR—Lipid cartridge cleanup,
11 naturally occurring PPL compounds were monitored.
Specifically, the ion fragment at m/z 184 was used to monitor
the PPLs in plasma extract after PPT and Captiva EMR—Lipid
removal.

O
CF3

O
CF3

S O

(CF2)nS O(CF2)n

NH

S O

H2C

NH2

R

COOH

n = 7 PFOSA

n = 7, R = Etn = 7, R = Me

Et-PFOSA-AcOH
Me-PFOSA-AcOH

0.5

0.1

10:2 FTA

6:2 FTA

PFBA

0.2

PFBS

0.3

8:2FTA

0.4

PFTrDA

PFHxA

0.6

PFDA
PFHxS
PFOS

0.7

PFPeA

Counts (%)

0.8

PFDoA

0.9

PFUDA

PFHpA

1.0

PFDS

PFOA

×102

PFNA

Figure 2. Molecular structures of PFASs.

PFODA

O

PFHxDAC

N-Me FOSAA
PFTeDA
N-Et FOSAA(CF2)nCF3

The MRM chromatogram of plasma spiked at 5 ng/mL(Figure 3) shows the chromatographic performance thatcan be obtained using the EMR—Lipid protocol. Even at the
5 ng/mL level, ideal peak shapes due to reduced matrix
effect and interferences resulted in good separation andsignal‑to‑noise (S/N) for accurate integration. Using the6495 Triple Quadrupole LC/MS system, accurate detection
and quantification at levels of 0.1 ng/mL and lower can beachieved when performing analysis of PFASs in plasma.

0
-0.1

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8

4.0

4.2

4.4

4.6

4.8

Acquisition time (min)Figure 3. MRM chromatogram of plasma spiked at 5 ng/mL.

5

As shown in Figure 4, approximately 99 % (based on peakarea comparison) of the PPLs monitored were eliminatedfrom the extracted plasma samples compared to PPT alone,some of which would have coeluted with the target analytes.
The high relative abundance of PPLs shown in Figure 4(red trace) subjects the detector to potential saturation andcould impact the quality of quantification. In addition, a highabundance of PPLs can contaminate an LC-MS system overtime.

Quantitative Performance
Calibration curve linearity was evaluated. Figure 5 shows
the calibration curves for PFOA and PFOS. Good linearity of
response was observed at the six concentration levels tested(0.1 to 50 ng/mL) on the three separate occasions that they
were generated. The average coefficient of determination (R2)for all PFASs studied was greater than 0.992, with linearity
from 0.1 to 50 ng/mL, linear fit, 1/x weighting.

Protein precipitation

Relative abundanceRelative response

EMR—Lipid cleanup

4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0

A

PFOA
y = 1.151382*x – 0.001099
R2 = 0.99892584

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0
Acquisition time (min)Figure 4. Overlay of MRM chromatograms of 11 PPLs monitored at m/z 184
after PPT only (red) and after Agilent Captiva EMR—Lipid cleanup (blue).

Relative response

0
4.0
3.6
3.2
2.8
2.4
2.0
1.6
1.2
0.8
0.4
0

0.4

0.8

B

1.2 1.6 2.0 2.4 2.8
Relative concentration

3.2

3.6

4.0

3.2

3.6

4.0

PFOS
y = 1.023620*x – 0.001654
R2 = 0.99910335

0

0.4

0.8

1.2 1.6 2.0 2.4 2.8
Relative concentration

Figure 5. Calibration curves. A) PFOA, B) PFOS. Concentration range
0.1–50 ng/mL in plasma using protein precipitation followed with
Agilent Captiva EMR—Lipid cleanup.

6

Method recovery and reproducibility (RSDs) for the 22 PFASs
were determined by spiking the standard into plasma at 5
and 20 ng/mL in replicates of five. Overall recoveries wereexcellent and between 75 and 125 % (Figure 6). Most PFASs
had recoveries of 90–110 %. The widely studied PFASs,
PFOS and PFOA, had average recoveries of 92.7 ±6.6 % and93.1 ±5.0 %, respectively, at both spiking levels in plasma.
Relative standard deviations were acceptable, and ranged
from 0.8 to 14 % at the 5 and 20 ng/mL levels.

125

5 µg/L spike
20 µg/L spike

Recovery (%)

100

75

50

25

PF
6: BA
2
F
PF TA
Pe
PF A
H
x
PF A
B
8: S
2
F
PF TA
H
p
PF A
OA
PF
H
x
P S
10 FN
:2 A
FT
A
N
M PFD
eF A
N O
Et SA
FO A
SA
PF A
O
PF S
Ud
PF A
D
PF oA
Tr
PF DA
Te
DA

0

Figure 6. Recovery and RSD for the PFASs evaluated at 5 and 20 ng/mL.

Conclusion
This Application Note presents a simple and rapid workflow
to prepare plasma samples for LC-MS/MS analysis of
PFASs. Extraction of 22 PFASs from plasma was performed
using in-well PPT followed by PPL removal using an Agilent
Captiva EMR—Lipid cartridge in a pass-through format.
Captiva EMR—Lipid efficiently removed 99 % of the unwanted
PPLs from the plasma matrix, with excellent recovery oftarget analytes. The sample extract was cleaner than using
PPT alone, thereby reducing the potential for ion suppression
and LC-MS/MS system contamination and downtime. In-well
PPT has the benefit of less sample handling and transfer.
Analysis of PFASs at 5 ng/mL yielded ideal chromatographic
peak shapes and good S/N. Response for PFASs over six
concentration levels (0.1–50 ng/mL) was linear, with an R2
greater than 0.992. Recoveries were excellent at 75 % or
higher, and RSDs less than 14 % for the PFASs tested. The
results showed the method to be acceptable for multiresidue
extraction and analysis of PFASs.
Captiva EMR—Lipid methodology can readily be incorporated
into existing research laboratory workflows, and does not
require additional sample preparation devices or glassware.
In either 96-well plate or 1 mL cartridge formats, Captiva
EMR—Lipid is compatible with automation, enablinghigh‑throughput applications. The frit design provides easy
and efficient elution of samples without clogging.

7

References
1.

United States Environmental Protection Agency. Research
on Per- an Polyfluoroalkyl Substances (PFAS). Retrieved
October 11, 2017. https://www.epa.gov/chemicalresearch/research-and-polyfluoroalkyl-substances-pfas

2.

Matuszewski, B. K.; Constanzer, M. L.; Chavez‑Eng, C. M.
Strategies for the Assessment of Matrix Effectin Quantitative Bioanalytical Methods Based on
HPLC‑MS/MS. Anal. Chem. 2003, 75(13), 3019–3030.

3.

Little, J. L.; Wempe, M. F.; Buchanan, C. M. Liquid
chromatography–mass spectrometry/mass
spectrometry method development for drug metabolism
studies: Examining lipid matrix ionization effects inplasma. J. Chromatogr. B 2006, 833, 219.

4.

Ismaiel, O. A.; et al. Monitoring phospholipids
for assessment of matrix effects in a liquid
chromatography–tandem mass spectrometry method
for hydrocodone and pseudoephedrine in human plasma.
J. Chromatog. B 2007, 859, 84–93.

5.

Chambers, E.; et al. Systematic and comprehensive
strategy for reducing matrix effects in LC/MS/MS
analyses. J. Chromatog. B 2007, 852, 22–34.

www.agilent.com/chem
For Research Use Only. Not for use in diagnostic procedures.
This information is subject to change without notice.
© Agilent Technologies, Inc. 2017
Printed in the USA, November 6, 20175991-8656EN

Using the Blood Exposome to
Discover Causes of Disease
Technical Overview
Clinical Research

Authors

Introduction

Stephen M. Rappaport, Ph.D.

Of the 52.8 million world-wide deaths in 2010, approximately two-thirds were causedby chronic diseases, mainly cardiovascular disease (> 15 million) and cancer(> 7 million) [1]. Thus, it is reasonable to ask whether chronic diseases are attributable to genetic factors, exposures, or some combination of the two. Data compiled by
the Swedish Family-Cancer Database indicate that genetic (G) risks for 15 common
cancers were 10% or less [2]. This suggests that approximately 90% of cancer risks
result from exposures (E) or G×E interactions.

Center for Exposure Biology
University of California, Berkeley
Anthony Macherone, Ph.D.
Agilent Technologies, Inc.
Visiting Scientist, Johns Hopkins
University School of Medicine

Despite the relatively small genetic risks for cancer and other chronic diseases,
exquisite tools are available to investigate G factors in studies of human diseases. In
fact, genome-wide-association studies (GWAS) currently measures more than one
million single nucleotide polymorphisms in 2,000–20,000 subjects. In contrast, individuals’ exposures are still inferred from personal interviews or self-administered questionnaires [3] much as they were a century ago. Since this disparity in characterizing
G and E makes it impossible to thoroughly investigate the GxE matrix, technologies
and methods for data-driven analysis of exposures must be developed [4].

The exposome represents the sum of all chemical exposures received by an individual
over a lifetime from both exogenous sources (food, pollutants, ionizing radiation,
drugs, lifestyle factor, infections) and endogenous sources (human and microbiota
metabolism, oxidative stress, lipid peroxidation, infections, and preexisting disease).
“As such, the exposome is everything that is ‘not the genome’ and serves as anumbrella for all of the traditional omes” [5]. Figure 1 illustrates the placement of the
exposome in the continuum between states of human health and disease.

Microbiota
Diet and nutrition
Pollutants
Drugs

Pre-existing disease
and infections

Healthy
Figure 1.

Behavior and lifestyle

Exposomeme

Diseasedd

Exposure contributions to chronic human disease.

A Two-stage Strategy for Discovering and Targeting
Causal Exposures
By focusing upon all circulating chemicals in the human body, the blood exposome
motivates studies of nongenetic causes of chronic diseases in much the same mannerthat GWAS explores genetic causes [6,7,4]. To this end, Stephen M. Rappaport,
Director of the Center for Exposure Biology at the University of California, Berkeley,describes a two-phase approach for interrogating the blood exposome to discoverimportant exposures in disease cases and controls and then to target these exposuresin follow-up studies with large populations [7].

2Professor Rappaport functionally defines the exposome as roughly 200,000 circulatingchemicals in human blood, including metals, small molecules, proteins, and foreign
DNA. By comparing untargeted profiles of blood exposomes between diseased andhealthy subjects, he suggests that we perform exposome-wide association studies(EWAS) to pinpoint discriminating chemicals [7]. After identifying these key chemicals and verifying their disease associations in independent samples of cases andcontrols, the chemicals can be used as biomarkers of exposures or disease progression in targeted analyses of blood from large populations. Thus, a successful strategy
for discovering and reducing harmful exposures requires an initial data-driven investigation (EWAS), to find promising biomarkers, followed by knowledge-driven studies
that use the biomarkers to elucidate exposure-response relationships (biochemical
epidemiology), sources of exposure and human kinetics (exposure biology) and mechanisms of action (systems biology). Prof. Rappaport stresses that this two-step strategy will lead to reduced exposures, improved public health, early diagnosis of diseases, and personalized medical interventions. Figure 2 illustrates the two-step strategy of EWAS followed by targeted studies.

Step 1

Step 2• Data-driven EWAS
• Identify markers• Knowledge-driven
investigations• Elucidate:- Exposure-response relationships (biochemical epidemiology)- Sources of exposure and human kinetics (exposure biology)- Mechanisms of action (systems biology)

Reduced exposures

Early diagnosis of
diseases

Improved public health

Personalized medical interventions

Figure 2.

Two-phase EWAS targeted analytical strategy to identify and reduce harmful exposures.

3

Measuring the Exposome
Enormous technical challenges must be confronted to achieve the necessary combination of extreme multiplexing, sensitivity, and throughput required for EWAS andfollow-up studies. It is desired to conduct untargeted EWAS with 10–50 µL of blood,
serum, or plasma from each of a few hundred subjects, and to perform targeted analyses of biomarkers in equivalent volumes of blood or serum from thousands of subjects. A relevant analytical platform for many EWAS would wed high-resolution liquid
chromatography (LC) or gas chromatography (GC) with a time-of-flight (TOF) mass
spectrometer (MS) having excellent sensitivity and mass accuracy. Follow-up investigations of promising biomarkers could then employ robotics and triple quadrupole MSwith multiple-reaction monitoring. Through rigorous selection of specimens fromprospective cohort studies, it is possible to differentiate biomarkers of causal
exposures from biomarkers of disease progression.
Blood exposome

ted

ge

tar

Un

Diseased versus
healthy subjects

Biomarkers
of disease
Pollutantbiomarkers
Endogenous
biomarkers

Biomarkers
of exposure

Drugbiomarkers

Dietary
biomarkers

Based on:S. Rappaport, Biomarkers, 2012, 17(6), 48: 3-9

Figure 3.

EWAS of blood offers differentiation of biomarkers of exposure (causal pathways) frombiomarkers of disease (reactive pathways).

Proof-of-concept studies have already identified unknown biomarkers of exposure forcolorectal cancer and cardiovascular disease and biomarkers of disease progression
for diabetes [7].

4Analytical Strategies
Clearly, dedicated platforms of nanoflow LC/TOF and LC/Triple Quad and GC/TOF,
GC/Triple Quad are essential to ensure the necessary sensitivity and precision forboth data-driven EWAS and targeted, high-throughput follow-up studies. To this end,
Ivanisevic and Zhu, et al. (2013) [8] reported the ability to measure more than 30,000
unique features from a single 100 µL sample of serum or plasma in an untargeted
analysis using reversed-phase LC and hydrophilic interaction chromatography (HILIC)in both positive and negative electrospray modes. The analytical methodology used
an Agilent 1200 Series LC system coupled to an Agilent 6538 UHD Accurate-Mass
Q-TOF LC/MS system, and is the approach needed for a small-molecule EWAS.

Figure 4.

The Agilent 1200 Series LC system and an Agilent 6500 Series Accurate-Mass Quadrupole
Time-of-Flight (Q-TOF) LC/MS system.

Another relevant analytical approach involves modifying the Agilent-Fiehn GC/MS
methodology [9] to incorporate an Agilent 7890B GC with an Agilent 7200 Q-TOF
system for investigating serum extracts derivatized with methoxyamine hydrochloride
and MSTFA. In post-acquisition processing, an Agilent retention-time-locked spectral
library of over 1,000 chemical entities can be used to annotate unknown features. The
data can be used independently or to orthogonally compliment and corroborate
LC/MS data.

5

Figure 5.

The Agilent 7890A GC with an Agilent 7200 Q-TOF system.

Once biologically relevant biomarkers are annotated and verified, targeted
high-throughput analyses are needed to measure biomarkers of causal exposures andbiomarkers of disease progression. A relevant example of such targeted analyses
involves measurement of endocrine disrupting chemicals, using the Agilent 7890B GC
with an Agilent 7000C Series Triple Quadrupole system [10]. Similar applications of theAgilent GC/MS system have been used in clinical research laboratories to measure
estrogen biomarkers in breast cancer studies at extremely low levels (for example,
17b-estradiol can be detected at 0.12 pg/mL (4.4E-7 µM)) [11].
Once data are collected, sophisticated bioinformatic software is required to compare
biomarker levels across populations and to investigate covariates. Agilent Mass
Profiler Professional (MPP) Software provides advanced statistical analysis and visualization tools for GC/MS, LC/MS, CE/MS and ICP-MS data analysis that can be used
to identify key features and perform global analyses. The analyst can further map
these to biological pathways using the Pathway Architect tools.

Reactive Electrophiles
Reactive electrophiles represent an important class of toxic chemicals that is produced from metabolism of xenobiotic and endogenous precursor molecules, oxidative
stress, and lipid peroxidation [12]. Although too reactive to measure directly in blood,levels of these electrophiles can be inferred by measuring adducts from reactions withprominent blood proteins such as human serum albumin (HSA). Prof. Rappaport’s
laboratory has proposed untargeted MS approaches for profiling adducts of HSA at
the nucleophilic hotspot, Cys34, as part of EWAS [13] and for identifying prominent
features by high resolution mass spectrometry [14].

6

1.5

1.0

0.5

0

Y-axis-1.5 -1.0-1.5-0.5 -1.0

-0.5

0

0.5

1.0

1.5

1.5

1.5

1.0

1.0

0.5

0.5

0

0

-0.5

-0.5

-1.0

-1.0

X-axis-1.5

Z-axis
-1.5

-1.0

-1.0
-0.5

-0.5

0

0

0.5

0.5

1.0

1.0
1.5

Figure 6.

1.5

Mass Profiler Professional illustrating separation of different chemotypes from GC/TOF datausing PCA analysis of ANOVA results.

Conclusion
Exposomics is a collaborative paradigm that brings together epidemiology, environmental toxicology, analytical chemistry, nutrition, and microbiology in an open accessmilieu. Since exposomics applies multiple analytical techniques (chromatography,
spectrometry, spectroscopy, sensor-array technologies) and bioinformatics to characterize individuals’ exposomes, it requires development of novel technologies toachieve the demands of high resolution, high sensitivity and high throughput for
EWAS and follow-up investigations. The current state of exposome research could becompared to that of genomics in the early nineties. By integrating several omic technologies (metallomics, metabolomics, proteomics, and metagenomics) with one unified objective, the nascent field of exposomics may well provide the missing links todisease causality and personalized medicine.

7References
1. R. Lozano, et al. (2012) "Global and regional mortality from 235 causes of deathfor 20 age groups in 1990 and 2010: a systematic analysis for the Global Burdenof Disease Study 2010" The Lancet 380(9859):2095-2128.
2. K. Czene, P. Lichtenstein, K. Hemminki (2002) “Environmental and heritablecauses of cancer among 9.6 million individuals in the Swedish Family-Cancer
Database” Int. J. Cancer 99(2):260-266.
3. B.K. Armstrong, et al. (1992) "Principles of Exposure Measurement in
Epidemiology" Oxford Med. Pubs.
4. National Institutes of Health (2012) “The individual exposome project”
Retrieved October 7, 2013 from:http://www.dpcpsi.nih.gov/council/pdf/Exposome_Project_concept%5B2%5D.pdf.
5. J. Pleil (2012) “Categorizing biomarkers of the human exposome and developing
metrics for assessing environmental sustainability” J. Toxicology andEnvironmental Health, Part B 15:264–280.
6. S.M. Rappaport, and M. T. Smith (2010) "Environment and disease risks"
Science 330(6003): 460-461.
7. S.M. Rappaport, (2012) "Biomarkers intersect with the exposome" Biomarkers
17(6): 483-489.
8. J. Ivanisevic, et al. (2013) "Toward ‘Omic Scale Metabolite Profiling: A Dual
Separation−Mass Spectrometry Approach for Coverage of Lipid and Central
Carbon Metabolism" Anal. Chem. 85:6876−6884.
9. M. Palazoglu, O. Fiehn (2009) "Metabolite Identification in Blood Plasma Using
GC/MS and the Agilent Fiehn GC/MS Metabolomics RTL Library" Agilent
Technologies, Inc. Application Note 5990-3638EN. Agilent Technologies, Inc.
Santa Clara, CA.
10. A. Macherone, M. Churley, (2013) "Monitoring steroidal analogues in clinicaland environmental chemistry: one model for exposomics" The 61st Conference
on Mass Spectrometry and Allied Topics, June 9–13. Minneapolis Convention
Center, Minneapolis, Minnesota.
11. C. Williard (2013) "Application of GC/MS/MS in Monitoring Steroids as a
Pharmacokinetic and Pharmacological Biomarker" Retrieved April 1, 2013 from,http://xtalks.com/xto525pharmanet-i3thankyou.ashx
12. S.M. Rappaport, et al. (2012) "Adductomics: Characterizing exposures toreactive electrophiles" Toxicology Letters 213(1): 83-90.

www.agilent.com/chem

13. H. Li, et al. (2011) ”Profiling Cys34 adducts of human serum albumin by fixedstep selected reaction monitoring” Mol. Cell Proteomics 10:(3):M110 004606.

For Research Use only. Not for use in diagnostic
procedures.

14. H. Grigoryan, et al. (2012) “Cys34 adducts of reactive oxygen species in humanserum albumin” Chem. Res. Toxicol. 25(8):1633-42.

Information subject to change without notice.

15. S.M. Rappaport, (2011) "Implications of the exposome for exposure science"
J. Exposure Science and Environmental Epidemiology 21: 5–9.

© Agilent Technologies, Inc., 2013, 2015
Printed in the USA
September 15, 2015
5991-3418EN

Poster Reprint

ASMS 2018
MP238

Analysis of Polycyclic Aromatic
Hydrocarbons (PAH) and Hydroxylated PAH
Metabolites in Plasma and Urine Using
High-Resolution GC/Q-TOF
Sofia Nieto1, Michael Armstrong2, Anthony Macherone1,
Marc Elie2,, Richard Reisdorph2, Nathan Eno1 and Nichole
Reisdorph2
1Agilent Technologies Inc., Santa Clara, CA ;2University of Colorado School of Pharmacy, Aurora, CO

7

Introduction

Experimental

Polycyclic aromatic hydrocarbons (PAHs) areproducts of incomplete combustion of organic
materials and present ubiquitously in the environment.
PAHs are well-know carcinogens, and the primarysources of human exposure to PAHs areenvironmental, dietary as well as occupational [1].
Hydroxylated metabolites of the PAHs are present attrace levels in biological matrices, and can be used as
biomarkers of the recent exposure to thesecompounds [1]. Because of high analytical sensitivity
requirements from the analytical methods for PAH
metabolite analysis, Triple Quadrupole GC/MS
instruments are preferred for their targeted analysis[2]. To add capability for untargeted analysis inaddition to a highly sensitive targeted approach fordetection of the persistent organic pollutants and their
metabolites, use of a high resolution accurate mass
7250 GC/Q-TOF system was evaluated in the current
study. Thus, we have applied a targeted approach forthe detection of PAHs and PAH metabolites, and an
untargeted approach to discover other biologically
relevant compounds in human urine and plasma
extracts.

Experimental

Six milliliters of pooled plasma and urine samples
were extracted with an equal volume ofhexane/acetone (1:1, v/v). To these, 1.1 g magnesium
sulfate and 1.1 g sodium chloride was added to assistin phase separation. The extracts were spiked withvarious concentrations of PAH and hydroxy-PAH
standards, ranging from 0.2 ppb to 2 ppm, as well as
deuterated internal standards. Dried samples were
derivatized with a mixture of MSTFA/pyridine andanalyzed using an Agilent 7890B GC system coupled
to a high resolution 7250 GC/Q-TOF, equipped with an
Electron Ionization (EI) source allowing low-energy
ionization (Figure 1). Instrument parameters areshown in Table 1.
The data were processed using MassHunter
Qualitative Analysis (B.08) as well as Quantitative
Analysis (B.09) software. Unknowns Analysis was
further used for the untargeted identification of
additional PAH-like compounds and other compoundsof potential interest not found in the target list.

8

Figure 1. Agilent 7250 GC/Q-TOF
GC and MS Conditions:
ColumnDB-5MS UI, 30 m, 0.25 mm, 0.25 µm

Injection volume
Injection mode
Split/Splitless inlet
temperature

1 µL
Splitless
270°C

70°C for 1 min
Oven temperature program 20°C/min to 270°C, 10°C/min to 307°C,
40°C/min to 325°C, 4 min hold
Carrier gas
Helium at 1.2 mL/min constant flow
Transfer line temperature 280°C
Standard EI at 70 eV
Ionization mode
Low Electron Energy EI at 15 eV and 12 eV
Source temperature
280°C (200°C for low electron energy)
Quadrupole temperature
150°C
Mass range
50 to 650 m/z
Spectral acquisition rate
5 Hz

Table 1. GC/Q-TOF conditions2

Results and Discussion

Quantitation and qualitative screening approach
As a first step, an accurate mass Personal Compound
Database and Library (PCDL) containing PAHs andhydroxylated PAHs in their derivatized form, (Table 2) wasconstructed (Figure 2) and used for automated creation ofa quantitative method.
Compound name
Naphthalene-d8
Naphthalene
Acenaphthylene
Acenaphthene-d10-IS
Acenaphthene
Fluorene
1-Hydroxynaphthalene
1-Hydroxynaphthalene-d7
2-Hydroxynaphthalene
Phenanthrene-d10
Phenanthrene
Anthracene
Fluoranthene
Pyrene3-Hydroxyfluorene
2-hydroxyfluorene-d92-hydroxyfluorene
4-Hydroxyphenanthrene
1,6-Dihydroxynaphthalene
3-Hydroxyphenanthrene-d9
3-Hydroxyphenanthrene
2,7-Dihydroxynaphthalene
1-Hydroxyphenanthrene
9-hydroxyphenanthrene-d8
9-Hydroxyphenanthrene
Benzo(a)anthracene
Chrysene-d12
Chrysene
1-hydroxypyrene-d9
1-Hydroxypyrene
Benzo(b)fluoranthene
Benzo(k)fluorantheneBenz(a)pyrene
Perylene-d12
3-Hydroxychrysene-d11
6-Hydroxychrysene
Indeno(1,2,3-cd)pyrene
Dibenz(a,h)anthracene
Benzo(ghi)perylene-d12
Benzo(ghi)perylene
3-Hydroxybenzo(a)pyrene-d11
9-Hydroxybenzo(a)pyrene
7,8-Hydroxybenzo(a)pyrene

RT

Formula of derivatizedcompound

5.485
5.507
7.366
7.54
7.58
8.216
7.838
7.799
7.99
9.37
9.4
9.46
10.86
11.14
10.18
10.24
10.29
10.76
9.75
11.03810.94
9.87
11.08310.89911.12
12.796
12.81
12.84912.76112.81614.48414.52914.981
15.06
14.521
14.08
16.557
16.59516.924
16.97
16.614
16.634
17.89

C10D8
C10H8
C12H8
C12D10C12H10C13H10C13 H16 O Si
C13H9D7OSi
C13 H16 O Si
C14D10
C14H10
C14H10
C16H10
C16H10
C16H18OSi
C16H9D9OSi
C16H18OSi
C17H18OSi
C16H24O2Si2
C17H9D9OSi
C17H18OSi
C16H24O2Si2
C17H18OSi
C17H10D8OSi
C17H18OSi
C18H12
C18D12
C18H12
C19H9D9OSi
C19H18OSi
C20H12
C20H12
C20H12
C20D12
C21H9D11OSi
C21H20OSi
C22H12
C22H14
C22D12
C22H12
C23H9D11OSi
C23H20OSi
C26H28O2Si2

m/z ofderivatizedcompound136.1123
128.0621
152.0621
164.1405
154.0777
166.0777
216.0965
223.1404
216.0965
188.1405
178.0782
178.0782
202.0777
202.0777254.1121
263.1686
254.1121
266.1121304.1309
275.1686
266.1121304.1309
266.1121274.1624
266.1121228.0934
240.1687
228.0934
299.1686
290.1121252.0934
252.0934
252.0934
264.1687
327.1968
316.1278
276.0934
278.1091
288.1687
276.0934
351.1968
340.1278
428.1622

Table 2. List of targeted compounds

Figure 2. Accurate Mass in PCDL format created for PAHs
and hydroxylated PAH metabolites.

The examples of matrix-matched calibration curves
for PAHs and their hydroxylated metabolites areshown in Figure 3.
In most cases, the calibration curves were linear up to2000 ng/mL. However, in a few cases, non-linearity
was observed above approximately 1000 ng/mL.

Urine

1-Hydroxynaphthalene

PlasmaFluorene

6-Hydroxychrysene

9-Hydroxybenzo(a)pyrene

Figure 3. Calibration curves examples for both urine
and plasma matrices (0.2-2000 ng/mL). EIC overlay
for concentrations 0.2-20 ng/mL is shown on theright.

Mass accuracy (Table 3) is shown for PAH and OHPAH in plasma matrix across a concentration range of
2-2000 ppb and on average was below 1 ppm.
LOD for both urine and plasma matrices were
calculated based on 5 replicate injections and areshown in Table 4.
In addition to quantitation, the PCDL was also utilized
in quick qualitative screening using MassHunter
Qualitative Analysis (shown in Figure 4).

Figure 4. Example of screening results using PCDL
MassHunter Qulitative software

93

Results and Discussion

Unknowns identification and confirmation of tentative hits
The untargeted workflow using Unknowns Analysis and NIST 17.L library identified several additional PAH-likecompounds and other xenobiotics, including 1-methyl-2-(phenylmethyl) benzene, benzophenone, pentochlorophenol,
Escitalopram, octocrylene, 1,12-dimethyl benz(a)anthracene and dibenz(a,j)acridine in urine and cotinine (biomarker of
exposure to tobacco smoke [3]) and 1-naphthoic acid in plasma (Figure 5A). Accurate mass information as well as
retention indices were utilized to confirm the candidate hit’s identity. A proprietary Electron Ionization (EI) source design
allowed for low energy ionization to assist in the confirmation of candidate molecular ions (Figure 5B).
Compound/concentration, ng/mL
Naphthalene
Acenaphthylene
Acenaphthene
1-Hydroxynaphthalene
2-Hydroxynaphthalene
Fluorene
Phenanthrene
Anthracene
1,6-Dihydroxynaphthalene
2,7-Dihydroxynaphthalene
3-Hydroxyfluorene
2-hydroxyfluorene
4-Hydroxyphenanthrene
Fluoranthene
3-Hydroxyphenanthrene
1-Hydroxyphenanthrene
9-Hydroxyphenanthrene
PyreneBenzo(a)anthracene
Chrysene
6-Hydroxychrysene
Benzo(b)fluoranthene
Benzo(k)fluorantheneBenz(a)pyrene
Indeno(1,2,3-cd)pyrene
Dibenz(a,h)anthracene
9-Hydroxybenzo(a)pyrene
Benzo(ghi)perylene
7,8-Hydroxybenzo(a)pyrene

LOD (ng/mL) per matrix
Mass Accuracy (ppm) per concentration
Urine Plasma
2
820
80 200 800 2000 Compound name

0.47
0.63
0.93
0.74
0.95
0.92
0.64
0.50
0.73
0.35
0.59
0.96
0.70
0.41
0.78
0.46
0.52
0.35
0.58
0.93
0.37
1.14
0.59
0.53
0.29
0.57
0.68
0.88
2.57

0.56
0.50
0.52
0.49
0.51
1.23
0.45
0.44
1.26
0.55
0.59
0.54
0.90
0.56
0.39
0.37
0.65
0.25
0.48
0.66
0.25
0.46
0.62
0.91
0.43
0.42
0.67
0.76
1.17

0.47
0.18
0.97
0.48
0.54
1.07
0.88
0.84
0.62
0.78
0.79
0.66
0.34
0.37
0.38
0.47
0.37
0.36
0.33
0.41
0.62
0.49
0.93
0.32
0.48
0.50
0.84
0.61
0.98

1.02
0.40
1.85
0.52
0.47
0.39
1.19
1.35
0.36
1.05
0.82
0.73
0.36
0.52
0.36
0.55
0.74
0.40
0.58
0.56
0.53
0.70
0.75
0.28
0.62
0.47
0.65
0.65
0.57

0.55
0.19
1.37
0.50
0.43
0.77
1.34
1.28
0.62
0.86
0.43
0.51
0.34
0.73
0.43
0.59
0.46
0.42
0.59
0.44
0.36
0.74
0.99
0.42
0.44
0.47
0.49
0.43
0.81

1.48
0.45
2.16
0.74
0.76
0.96
0.64
0.80
0.40
0.41
0.40
0.49
0.38
0.73
0.21
0.37
0.26
0.70
1.11
0.92
0.20
0.92
0.21
0.44
0.47
0.98
0.45
0.61
0.48

0.84
0.79
3.37
0.86
1.07
1.45
0.61
0.46
0.56
0.53
0.34
0.34
0.59
0.71
0.38
0.35
0.45
0.51
1.71
1.51
0.60
0.98
0.55
0.76
0.78
1.06
0.51
0.35
0.93

Table 3. Mass accuracy in plasma observed for
PAHs and hydroxylated PAH metabolites acrossconcentrations of 2-2000 ppb.

Naphthalene
Acenaphthylene
Acenaphthene
1-Hydroxynaphthalene
2-Hydroxynaphthalene
Fluorene
Phenanthrene
Anthracene
1,6-Dihydroxynaphthalene
2,7-Dihydroxynaphthalene
3-Hydroxyfluorene
2-hydroxyfluorene
4-Hydroxyphenanthrene
Fluoranthene
3-Hydroxyphenanthrene
1-Hydroxyphenanthrene
9-Hydroxyphenanthrene
PyreneBenzo(a)anthracene
Chrysene
6-Hydroxychrysene
Benzo(b)fluoranthene
Benzo(k)fluorantheneBenz(a)pyrene
Indeno(1,2,3-cd)pyrene
Dibenz(a,h)anthracene
9-Hydroxybenzo(a)pyrene
Benzo(ghi)perylene
7,8-Hydroxybenzo(a)pyrene

0.52
0.42
0.98
0.48
0.28
1.12
0.35
0.36
0.34
0.13
0.22
0.25
0.39
0.30
0.60
0.34
0.50
0.42
0.16
0.26
0.14
0.38
0.38
0.61
0.65
0.51
0.47
0.89
6.65

0.38
0.74
1.28
0.47
0.20
0.26
0.18
0.26
0.16
0.10
0.36
0.21
0.42
0.19
0.27
0.30
0.33
0.10
0.23
0.09
0.16
0.26
0.27
0.30
0.35
0.09
0.34
0.36
3.63

A

B
70 eV
15 eV
12 eV

Figure 5. Unknowns Analysis
Table 4. LOD calculated for results showing an example of a
PAH and PAH metabolites in tentatively identified compound inplasma and urine matrices urine matrix using NIST17.L (A) as
based
on
5
replicate well as 70 eV and low electronenergy spectra for the tentatively
injections.
identified compound (B).

Conclusions

Targeted quantification and untargeted screening for PAHs, their metabolites, and other substances in complex
biological matrices was successfully performed using an HRMS GC/Q-TOF.
References
1 Strickland MK, Kang D, and Sithisarankul P. Environ Health Perspect., 1996, 104(Suppl 5): 927–932.
2 Gupta P, et al., Journal of analytical toxicology. April 2015, 39(5).
3 Benowitz NL, Epidemiol Rev. 1996;18(2):188-204.

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Published in the USA, November 28, 20185991-6489EN