Comprehensive Food Profiling
Combining High Resolution LC/MS
and GC/MS Analyses
Application Note
Metabolomics

Authors

Abstract

Zijuan Lai, Mine Palazoglu, andA comprehensive untargeted approach was applied to studying differences in foodOliver Fiehn

compositions from three distinct diets. To achieve a broad coverage of metabolite

University of California at Davisclasses, this study used high-resolution accurate-mass LC/MS and GC/MS.

Sofia Nieto, Yuqin Dai, andMark Sartain
Agilent Technologies, Inc.

These full spectrum mass spectrometers allowed for untargeted acquisition of
widely varying metabolites. Data acquired by the different analytical techniques
were processed using a common software platform, Agilent MassHunter, withmultivariate analysis performed in Agilent Mass Profiler Professional (MPP). The
results revealed distinct groups of metabolites characteristic of certain diets
evaluated in this study.

Introduction

Experimental

Analysis of food components is essential
to evaluate food nutritional quality
and safety. This may also serve otherpurposes such as authentication anddifferentiation1. Although the effect ofdiet on human health is well recognized,
the complete set of small molecules
present in distinct diets is largely
unknown2.

Materials and sample preparation

The goal of the study was to compare
three food plates referred to in thisstudy as fast food (FF), pesco-vegetarian(PV), and eastern vegetarian (EV), that
represent different diets with different
chemical compositions. The differential
analysis approach used in metabolomics
is ideal for use in many areas of foodscience and nutrition research3. This
comprehensive untargeted approach
was applied to study the differences
between these three plates, focusing on
the detection of as many metabolites as
possible1,4.
To identify food components including
small polar molecules, nonpolar
molecules, and complex lipids, multiple
analytical methods were used, andleveraged Agilent 7200 GC/Q-TOF,
Agilent 6230 LC/TOF, and Agilent 6550
LC/Q-TOF systems. Agilent MassHunter
software was used to efficiently process
both GC/MS and LC/MS data withdifferential analysis performed using
Agilent Mass Profiler Professional (MPP),a chemometrics software tool.
The multiple analytical techniques,in combination with the advanced
differential software workflow, wascrucial to perform comprehensive
analysis and data interpretation.

LC/MS Metabolomics

Food ingredients from each plate(Table 1) were mixed, homogenized,
and freeze‑dried. Lyophilized material
was extracted with 80:20 (v/v)
methanol/water, and centrifuged at
27,000 xg for 5 minutes at 4 °C topellet proteins. For LC/MS samples,the supernatants were filtered throughprewashed Pall Omega 3kDa Nanosep
centrifugal devices. For GC/MS analysis,
the extracts were dried and derivatizedwith 40 mg/mL methoxyaminehydrochloride in pyridine, followed bysilylation with MSTFA + 1 % TMCS. Sixtechnical replicates were used for eachtechnique.

Instrumentation andanalytical methods
GC/MS Metabolomics
Both electron ionization (EI) and chemical
ionization (CI) spectra were acquired
using an Agilent 7890B GC coupled toan Agilent 7200 series high-resolution
accurate mass GC/Q-TOF. The data wereacquired at 10 and 15 Hz to ensure best
performance of the feature detection
algorithm used for data processing.
Further analytical methods and conditions
for GC/Q-TOF were used as described
elsewhere5.

An Agilent 1290 Infinity LC, coupled
to either an Agilent 6230 TOF, or an
Agilent 6550 iFunnel Q-TOF, was used fordata acquisition. The reversed‑phase (RP)chromatographic separation was
conducted on an Agilent ZORBAX
SB‑Aq column (2.1 × 50 mm, 1.8 µm,
p/n 827700-914). The aqueous
normal phase (ANP) chromatographic
separation was performed on a Cogent
Diamond Hydride HPLC column(2.1 × 150 mm, 4 µm). Data wereacquired in both positive and negative
ion electrospray ionization (ESI) modes.
Further LC/MS conditions used in thisstudy are described elsewhere6,7.

LC/MS Lipidomics
A 1290 Infinity LC was coupled to a
6550 iFunnel Q-TOF equipped with a dual
Agilent Jet Stream source. Data wereacquired in both positive and negative ion
modes. Reversed-phase chromatographic
conditions and MS acquisition
parameters were replicated as described
elsewhere8 with the changes shown in
Table 2.

Table 1. Content of the food plates used in this study.
Fast food plate

Pesco-vegetarian plate

Eastern vegetarian plate

Hamburger• Bun• Beef• Cheese• Bacon• Lettuce• Tomatoes• Pickles• Ketchup

SalmonWhite rice

Blended brown riceFried egg

Sliced almonds

Sesame seeds

Lemon slices

Spicy sauce

Steamed vegetables• Carrots• Broccoli• Onions• Cabbage• Red bell pepper

Tofu

Grapes

Zucchini

YogurtRadishBlueberries

Rice punch

French fries
Baked beans
Chocolate chip cookies
Coke (regular)

Green tea

2

SpinachBean sprouts
Soy sprouts
Carrots

Data analysis software

The Agilent MassHunter data analysis
suite (B.07.00) and MPP (B.13.0)
were used for differential analysis.
Identification of the differential
features was accomplished using theAgilent-Fiehn Metabolomics library,
Agilent METLIN Personal Compound
Database, NIST14 library, and SimLipid
4.40 (PREMIER Biosoft).

Data analysis workflow

Figure 1 summarizes the data analysis
workflows.

Feature extraction
The initial data processing involved
feature extraction for the subsequentdifferential analysis. LC/MS
metabolomics and lipidomics data wereanalyzed in MassHunter Profinder inthe MassHunter data analysis suite(Figure 2A)9. GC/MS data were processedusing the Unknowns Analysis tool in
the MassHunter data analysis suite(Figure 2B).

Differential analysis and initial
compound identification/annotation
After feature extraction was performed,
the features from GC/MS and
LC/MS data were imported into MPP fordifferential analysis. ID Browser within
MPP was used for either initial compound
annotation (LC/MS), or identification(GC/MS). In the case of GC/Q-TOF
EI spectra, both the NIST14 and theAgilent‑Fiehn Metabolomics libraries
were used to identify compounds.
For LC/MS metabolomics datasets,compound annotation was performedby querying the Agilent METLIN PCD
or other user-created databases.
For lipidomics datasets, compound
annotation was performed in SimLipid
software, and then lipid annotations were
imported into MPP.

Table 2. Chromatographic conditions for LC/MS lipidomics data acquisition.
Parameter

Value

LC flow

Held at 0 %B from 0–3 minutes,
to 20 %B at 5 minutes,
to 27.5 %B at 20 minutes,
to 70 %B at 25 minutes,
to 90 %B at 33 minutes,
held for 1 minute,back to 0 %B at 35 minutes

Stop time

35 minutes

Post time

4 minutes

Drying gas

200 °C at 15 L/min

Sheath gas

300 °C at 11 L/min

Nebulizer gas

45 psi

Fragmentor

380 V

Auto MS/MS acquisition speed

4 spectra/s (MS)
4 spectra/s (MS/MS)

Auto MS/MS collision energy

20 and 35 eV

Featureextraction

Dataacquisition

Differential data analysis
CompoundAnnotation/Identification

Unknowns andtentative hits

Unknowns
Analysis
GC/MS

MPP
Profinder

Qual/MSC
SimLipid
lipidomics

LC/MS
Figure 1. Data analysis workflows.

3

Solvent blank

Example: Cpd Group 77
Ceramide (d18:1/24:1), ∆ -0.46 ppm

PV

EV

FF

Figure 2A. Feature extraction: LC/MS data files were analyzed with Agilent MassHunter Profinder software using the recursive batch feature extraction
algorithm. Shown is a representative extracted feature, later identified as ceramide (d18:1/24:1), and the relative compound abundance across all food platesamples in the lipid dataset.

Component spectrum
TIC
Component
EICs

Library spectrum

Figure 2B. GC/Q-TOF data files were processed using Agilent Unknowns Analysis for feature finding and library search.

4

Lipid identifications of LC/MS/MS
lipidomics datasets were performed with
SimLipid, which match lipid in silico
MS/MS fragments for structure-specificcharacteristic ions.

Results and Discussion
Maximizing the coverage ofcompound classes using multiple
techniques, analytical methods,
and polarities

For LC/MS, different analytical methods
were used to cover the broad range of
metabolites. In this study, optimized
conditions were developed to provide
the best separation and detection.
Table 3 summarizes the thousands offeatures detected for the different LC/MS
methods. To illustrate the importance ofusing multiple LC/MS methods, RP-ESI
metabolomics data were compared for
ESI positive or negative ion mode. Only
8 % of features were detected in bothmodes, demonstrating that many features
would be missed if only a single polarity
were used.
The data collected on the various
Agilent systems showed a high level of
reproducibility, a critical component for
reliable differential analysis. Figure 3
shows an example from positive ion
reversed-phase LC/TOF MS data;approximately 90 % of features weredetected and extracted with CV of ≤30 %.
The highly reproducible assay results
provide high confidence for subsequentstatistical analysis.

LC/MS Method

Compounds targeted

No. of features
No. of features
positive ion mode (+) negative ion mode (–)

ANP-ESI

Metabolites (polar)

2,957

2,005

RP-ESI

Metabolites (intermediate)

3,279

2,135

Lipid-RP-AJS

Lipids

2,689

3,816

ANP-ESI: Aqueous-normal phase chromatography coupled to the electrospray source
RP-ESI: Reversed-phase chromatography coupled to the electrospray source
Lipid-RP-AJS: Reversed-phase chromatography coupled to the Agilent Jet Stream source
1,000
900

Cumulative % 120 %

PV
879

800
Number of features

When either no match, or a poor MS/MS
library match was returned, Molecular
Structure Correlator (MSC) was used
to propose matches for annotatedmetabolites from both GC/MS/MS and
LC/MS/MS data.

Table 3. Comparing the number of features found from food plates by different analytical approaches
illustrated the importance of using multiple LC/MS methods and different polarities. Note that eachfeature represents multiple adducts and isotopes for a single compound.

89 %

728

700

94 %

97 %

98 %

98 %

99 %

99 %

99 % 100 %

500

60 %

400 57 %

329

300

40 %
162

10

20

1,200

EV

1,000

963

30

40

50

42

19

13

6

4

60
70
Bin (CV%)

80

90

100

28
>100

90 %

92 %

95 %

96 %

97 %

98 %

99 %

99 % 100 %

60 %

63 %

40 %
197

200

1,200

10
FF

1,000

20

30

87

91

40

50

20 %
35

36

23

16

7

60
70
Bin (CV%)

80

90

100

39
>100

91 %

94 %

96 %

97 %

98 %

98 %

99 %

99 % 100 %

82 %

600

60 %
40 %

280

200

112
10

100 %
80 %

51 %
469

400

0%

Cumulative % 120 %

1,015

800

0

100 %
80 %

685

400

0

0%

Cumulative % 120 %

84 %

800
600

20 %

79

100
0

100 %
80 %

79 %

600

200

Number of features

For LC/MS/MS metabolomics data,
MS/MS spectral library searching was
performed against multiple Agilent PCDLs
for compound identification.

Number of features

Compound identification

20

30

40

20 %
53

48

21

8

17

8

50

60
70
Bin (CV%)

80

90

100

35
>100

0%

Figure 3. Histograms from RP-positive LC/TOF MS data demonstrated highly reproducible assay results.

5

Statistical analysis and annotation
using MPP

Statistical analysis of untargeted
profiling data presents substantial
challenges because of the complexityof the compound profiles, and the sizeand variability of the data sets. MPP, asophisticated chemometrics softwarewith a variety of statistical tools, wasused in this study for differential analysis.
Principal Component Analysis (PCA) is
one of the most common unsupervised
data analysis methods used to detect
A

PV

EV

data patterns, and facilitate the evaluationof data quality. In this study, the PCA
plot shows tight clustering between thesample replicates in each food plate
for all analytical techniques indicating
good repeatability, and demonstrating
significant differences in the compositionof each plate (Figure 4).
Venn diagrams were used to compare
the composition of the different plates
in this study. Figure 5 shows that alarge number of compounds was shared

PV

B

FF

GC/Q-TOF EIEV

between food plates. The PV plate had
the highest number of unique metabolites
by all techniques. To further investigate if
the shared features (Figure 5B) showedstatistically meaningful differences
between food plates, a two-way ANOVA
was performed. We found that 1,131 outof 1,362 features were different (P ≤0.05).
More than 200 of the shared features hada ≥5-fold difference in abundance whencomparing PV with EV, PV with FF, andEV with FF plates.

PV

C

FF

EV

FF

Lipids
LC/Q-TOF
Negative

LC/TOF RP-Positive

Figure 4. The PCA plot of the food plate data demonstrates excellent repeatability across all three techniques, as well as highlighting the distinct nature of thesamples.

A
PV

B
90

376

207

EV

PV

C
234

568

286
95

411

EV

PV

410

491

1,362
122

125

219

1,992
172

169

307

210

407

201

FF

FF

FF

Figure 5. Number of compounds found by each technique in each plate represented by Venn diagrams. A) GC/Q-TOF, B) LC/TOF, RP-positive mode,C) LC/MS lipids. The highest number of unique components was found in the pesco-vegetarian (PV) plate.

6

EVCorrelation analysis provides anotheruseful tool to help reveal or confirm
relationships between samples in anexperiment. Figure 6 demonstrates therepeatability between sample replicatesin each food plate, confirming the PCA
assessment.

Color range

Correlation coefficient: 0.81

FF

EV

PV

PV

EV

FF

Figure 6. Example of sample correlation analysis for negative-ion LC/MS lipid analyses. The scatterplot shows the abundance correlation between
two sample replicates from the PV plate.

7

MPP also incorporates ID Browser, a toolthat allows users to annotate features
based on accurate mass information anddatabase matching using Agilent METLIN
PCD or any customer‑created databaseusing Agilent PCDL Manager software.

Figure 7A shows the compound
annotation results using MPP ID Browser
and database matching. Using thisapproach, compound classes can be
categorized (Figure 7B) by searchingdifferent types of databases.

Figure 7A. Compound annotation of features found in the food plates using ID Browser and database matching.

126

108 1178

232

Flavonoids "custom" (3447)
Hydroxycinnamic acids "custom" (41)
Agilent-Metlin Metabolites (30232)
Agilent-Metlin Lipids (39004)
Agilent-Pesticides (1684)
Agilent-Mycotoxins (455)Agilent-Vet Drugs-Food (1081)

1,080

1,537

Figure 7B. Annotation results from the LC/TOF data acquired using the reversed-phase positive ion
method. The numbers in parenthesis represent the size of the database, and the numbers in the pie chart
represent the number of compounds matched to the database.

8In a similar manner, lipidomics datasets
within MPP were annotated to categorize
lipid classes based on accurate massinformation using SimLipid. From MPP
results, the summed relative abundancesof the annotated lipid classes wereused to compare lipid profiles from thedifferent food plates (Figure 8). The
analyses revealed interesting differencesin lipid class abundance (Figure 8A),
fatty acid profiles (Figure 8B), andlipid saturation levels (Figure 8C). One

Lipid class compositions

A

PV

SM
2%
Ceramide
3%

PG
3%

PA PS
6% 1%

PE
9%

FF

PC
42%

Ceramide
2%

PA
1%
SM PG
PS
1%
2% 2%

EV
Ceramide
4%

PE
11%

Fatty acids
17%

B
35 %

Free fatty acid distribution

30 %
25 %
20 %
15 %
10 %
5%
0%

PA
PG 0% PS
2%
SM 5%
4%

PE
16%

PC
54%

Fatty acids
12%

PI
17%

notable observation was that the PV
plate contained higher levels of thepolyunsaturated lipids 22:6 (DHA) and
20:5 (EPA), while the FF plate contained
the highest level of saturated lipids.

PC
51%

Fatty acids
6%

PI
15%

PI
12%

C
PV
EV
FF

100 %
90 %
80 %
70 %
60 %
50 %
40 %
30 %
20 %
10 %
0%

Fatty acid saturation content

Saturated
Monounsaturated
Polyunsaturated

PV

EV

FF

14:0 16:1 16:0 18:3 18:2 18:1 18:0 20:4 20:5 22:6 22:5

Figure 8. Relative comparisons of lipid compositions. Negative-ion LC/MS lipid data were annotated with Agilent-METLIN PCD, and compound abundances wereused to compare relative differences in A) lipid class composition, B) free fatty acid profiles, and C) lipid saturation content.

9

A volcano plot is another tool in MPP thatallows a pair-wise comparison between
different groups (food plates in this study)
where the results can be visualized byfold change and significance. A volcano
plot for GC/Q-TOF data is shown in
Figure 9, demonstrating the difference inthe metabolite content between the EV
and PV plates.
While many antioxidants, such as
sinapinic acid, quinic acid, and gallic acid,among others, were present at higher
levels in the PV plate, some of the othercharacteristic metabolites (componentsof the specific ingredients present in
the EV plate) were present at higher
levels in the EV plate compared to the
PV plate. Examples of these metabolites
are raffinose, present at high levels inbeans, and daidzein, typically found in soy(Figure 9).

Pesco-vegetarian

Compound identification with
MS/MS data

Accurate mass MS/MS information
provided by high-resolution Q-TOF
systems is valuable for compound
identification and confirmation oftentative hits, as it allows more accurate
assignment of formulas for fragments
derived from selected precursors.
For GC/MS metabolite analysis,
EI spectra from GC/Q-TOF provide
significant structural information, but therelatively low abundance of the molecularion can complicate identification of
unknowns. To identify and confirm themolecular ion, CI spectra from GC/Q-TOF
were also acquired.

The compound identification andconfirmation workflow for GC/Q-TOF
using CI MS and EI MS/MS can bedemonstrated with caffeine, which
was tentatively identified by searching
the NIST14 library. Caffeine eluted
in an extremely congested part ofthe chromatogram (Figure 10A),
and its identification in this type of
extract represents a challenge for any
compound‑finding algorithm. In CI mode,an abundant molecular ion, as well
as isotopes, were observed with thecorrect mass and expected isotope ratios
corresponding to caffeine (Figure 10B).
Since an authentic MS/MS spectrum
for caffeine was not available, further
confirmation of the match was done with
MSC software (Figure 10C).

Vegetarian
Raffinose

Sinapinic acid20
NiacinEpigallocatechin–log10(corrected P value)

Gallic acid

Daidzein
15

Sesamin10

Quinic acid

Ferulic acid5

Tartaric acid

Caffeic acid

-20

-10

0
log2(fold change)

1020

Cholesterol

Figure 9. Example of a volcano plot in MPP shown for GC/Q-TOF data. A few selected differential compounds are labeled.

10When MS/MS is performed by selecting
a molecular ion as a precursor, thecompound information from MassHunter
Qualitative Analysis can be imported
to MSC for structural elucidation.
Based on the tentative molecular ion
formula(s) of the unknown or tentatively
identified compound, MSC searches
a user-specified database for any
possible structures corresponding to the
proposed formula or observed mass. It

then evaluates the observed MS/MS
fragmentation versus the possible
fragments for the proposed match.
The resulting MSC score accounts formass accuracy for the precursor andfragment ions and how well the observed
fragmentation is explained by the
proposed match. In this example, caffeine
had the highest number of literaturereferences, a mass error of 0.1 ppm, andan overall score of 89.7 (Figure 10C).

Figure 10D summarizes the GC/Q-TOF
compound identification results. The
highest amounts of antioxidants andflavonoids were observed in the PV plate,followed by the eastern vegetarian EV
plate. The FF plate was characterized bya high number of the pyrolysis productsthat results from the heat-processing offoods, the highest number of sugars andsaturated fats, and a lower number of
unique amino acids.

6

10

18.00
118.
18
88..0000

220.00
20
0.00
00

24.00
224.
24
44..00000

26

30.6826

28.4086
28.9006
29.3379
29.86652830

32.7710222.00
22.
22
2 000

32

34

35.4955

16.00
116.
16
66..000

33.648134.1741
34.6099

14.00
14
114.
44..00.00
00

27.4133

119.
19
9.90
900993909
19.9093

111.
111.
1.94
943
943
436
11.9436112.00
12
22..0000

25.7520
26.3116

19.2376119.
1999.2237
2337
376

15.92
15.92989.90078

10.3136
10.9277

7.26508.63769.35386.8438Counts

3.5
3.0
2.5
2.0
1.5
1.0
0.5
0

7.48608.0278

A ×10736

Acquisition time (min)195.0878([C8H10N4O2]+H)+

195.0878([C8H10N4O2]+H)+

CountsCountsB

195.2145

196.0903([C8H10N4O2]+H)+

197.0952([C8H10N4O2]+H)+

195.0 195.2 195.4 195.6 195.8 196.0 196.2 196.4 196.6 196.8 197.0 197.2
223.1189
235.1188([C8H10N4O2]+C2H5)+([C8H10N4O2]+C3H5)+
205.0906
192 196 200 204 208

212 216 220 224 228 232 236 240
Mass-to-charge (m/z)

C

Figure 10. Compound identification workflows and results for GC/Q-TOF. A) EI TIC overlaid with components EIC from Agilent Unknowns
Analysis. B) CI spectrum, showing methane adducts of the molecular ion with overlaid theoretical isotope pattern automatically assigned
by Molecular Formula Generator (MFG) in Agilent MassHunter Qualitative software. C) ID confirmation using MS/MS and Molecular
Structure Correlator (MSC).

11

To validate the initial annotations fromthe LC/MS metabolomics data, targeted
MS/MS experiments were performed
for those metabolites of interest using a6550 iFunnel Q-TOF LC/MS. Compound
identification and structure elucidation
were achieved using either MS/MS
library matching or structure correlation
using Molecular Structure Correlator(MSC)6,10. The MS/MS spectra weresearched against Agilent METLIN PCDL,and the spectral comparison is shown,as demonstrated for the identificationof L-glutamate (Figure 11A). The mirror
image plot (middle) showed excellent
matches between the acquired sample
spectrum (top) and the library standard
spectrum (bottom) for both m/z values
and abundances of the precursor andfragment ions with a reverse match scoreof 96. Using this approach, many foodcomponents were identified. Betaine andL-phenylalanine were found in all foodplates, daidzein was found in both PV
and EV plates, quercetin 3-galactosideand neohesperidin were found in only the
PV plate, and genistein was only foundin the EV plate. A mycotoxin metabolite,
mycrosporin/physcion, was identified in
the PV and EV food plates.
D

FF
PV

EV
Pyrolysis products

Sterols

Vitamins

Unsaturated fatty acids

Organic acids

Flavonoids

Saturated fatty acids

Amino acids

Antioxidants

Nucleobases

Lignans

Sugar alcohols

Lactones

Sugars and sugar phosphates

Figure 10D. GC/Q-TOF compound identification results summary.

Sample MS/MS spectrum

Mirror image plot

Match score: 96

Library MS/MS spectrum

Figure 11A. Compound identification using LC/Q-TOF MS/MS spectra matching with a reverse score of 96 for L-glutamate inpositive ion ESI mode. The reverse score reflects ion matches for those peaks from the library standards that are reflected in the
spectrum acquired from the sample. The collision energy (CE) of 10 eV was used to acquire the sample MS/MS spectrum.

12

When an acquired LC/MS/MS spectrum
had poor matches or no matches to theAgilent MS/MS library, MSC was used
for putative compound identification.
Based on the tentative molecular ion
formula(s) of the unknown or tentatively
identified compound, MSC searches auser-specified database for any possible

structures corresponding to the proposed
formula or observed mass, then evaluates
the observed MS/MS fragmentation
versus the possible fragments for the
proposed match. Figure 11B shows anexample of an MSC search result wherea total of 22 possible structure matches
were found for the unknown highlighted

22 structure hits foundQuercetin 3-rhamnosyl-(1→6)glucosyl-(1→6)galactoside• C33H40O21• m/z 773.2135• Mass error (MS): –0.9 ppm• Mass error (MS/MS): <2ppm• MFG score: 99.1
• MSC score: 94.9
• Elucidated 90 % ionsFigure 11B. MSC results obtained by searching Agilent PCDL.

13

with blue. The top match, quercetin
3-rhamnosyl-(1→6)glucosyl-(1→6)galactoside, showed a high MSC score,
and accounted for a high percentage ofthe observed fragment ions, suggestingthat this is a likely match for the
unknown.

LC/MS/MS lipidomics identifications
were accomplished by searching
extracted MS/MS spectra with
SimLipid software. Figure 12 shows
an example where two isomers ofphosphatidylethanolamine (PE) wereidentified, and the MS/MS data afforded
additional structural information. This
high-confidence lipid identification
approach can be used to constructalternative workflows to profile lipid

Compound 1
M = 739.5151∆ –0.14 ppm PE 36:4
12.17

Counts

×107
1.2 A
1.0 EV (ion mode)

Confirmed PE 18:2|18:2

Compound 2
M = 739.5150∆ –0.26 ppm PE 36:4

0.8
0.6

Confirmed PE 16:0|20:4

13.62

0.4
0.2
0

classes in more detail (Figure 13). A
custom subset lipid PCDL can be curated
with retention time values from identified
lipids, and this PCDL can be leveraged
in Profinder for a recursive targeted
workflow (Figure 13A). In this manner,
the PE lipid class was profiled in depth,and significant differences were observed
across the three food plates, even acrossPE isomers (Figure 13B).

14.75
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 31 32 33 34 35
Acquisition time (min)

B

C
18:2

(–) MS/MS

20:4(–) MS/MS

M
M

16:0

Figure 12. Lipid MS/MS Annotation with SimLipid. Two isobaric lipids were resolved by retention time, and automated identification with SimLipid confirmed
these compounds as distinct PE 36:4 isomers with different fatty acyl compositions (PE 18:2|18:2 and PE 16:0|20:4).

14

A

Auto
MS/MS

UpdatePCDL with RT

SimLipid

Profinder
on MS1 data

Phosphatidylethanolamine (PE) distribution
PV
EV
FF

20 %

15 %

10 %

Lyso PE

PE(P-18:1-22:6)

PE(P-16:0-22:6)

PE(P-18:0-18:2)

PE(P-16:0-20:4)

PE 20:4-22:6

PE 22:6-18:1

PE 22:6-18:2

PE 16:0-22:6

PE 18:0-18:1

PE 18:1-18:1 isomer 2

PE 18:1-18:2

PE 18:0-18:2 isomer 1

PE 16:0-20:4 isomer 2

PE 36:5 isomer 2

PE 18:2-18:2 isomer 1

PE 18:3-18:3

PE 36:5 isomer 1

PE 16:0-18:1

PE 16:0-18:2

PE 16:0-18:3

LPE 22:6

LPE 18:0

LPE 18:1

LPE 18:2

0%

LPE 18:3

5%

LPE 16:0

Percentage of total PE compound abunanceB 25 %

Ether-linked PE

Diacyl PE

Figure 13. Targeted workflows for in-depth lipid profiling. A) PE lipids were identified with high confidence from MS/MS dataacquired from pooled plate replicates. Observed retention times were used to curate a subset PE PCDL, which was used as
the source for Profinder Batch Targeted Feature Recursion for individual plate replicates. B) Results showing PE distribution
showing the relative abundances of lyso-PEs, Diacyl PEs, and ether-linked PEs profiled in depth across the three food plates.
Error bars = ±1 SD.

15

Conclusion

References

Because of the complex nature offood, no single extraction, separation,or ionization technique can cover allcompounds. The Agilent portfolio of
both LC and GC high-resolution accuratemass TOF-based mass spectrometers
enabled the comprehensive analysisof metabolites and lipids with a wide
range of physiochemical properties,
using a common, powerful data analysis
platform. Multiple analytical techniques,in combination with the advanced
differential analysis software workflow,
were crucial to successfully analyzing
and interpreting the food profiling results,
confirming the diverse nature of the threefood plates.

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This information is subject to change without notice.
© Agilent Technologies, Inc., 2017
Published in the USA, August 31, 20175991-8312EN