MSMSTips No. 390
GC-TOFMS Application

Structural Analysis of Additives and Related Compounds in Vinyl
Acetate by using a Py-GC-HRTOFMS and msFineAnalysis AI
Product used ： Mass Spectrometer (MS)Introduction

Electron ionization (EI) is one of the most popular ionization methods used in gas chromatography-mass spectrometry (GC-MS).
Consequently, compounds are typically identified by a mass spectral database search using EI mass spectra. Because molecularions are often weak or absent in 70 eV EI mass spectra, identification of unknowns can be difficult by EI alone. In these cases, soft
ionization (SI) can be very helpful for producing and identifying molecular ions. Recently, JEOL began developing an integrated
qualitative analysis workflow that automatically combines and interprets the information from EI and SI data. And then in 2018, we
introduced our integrated qualitative analysis software “msFineAnalysis” which uses both EI and SI data to improve compound
identification for GC-MS applications.
Despite the fact that msFineAnalysis was automatically able to determine the molecular formula and partial structure informationfrom EI fragment ion formulas, the actual structural formulas still required manual analysis using chemical compositions. To address
this, we then developed an automated structure analysis software package entitled “msFineAnalysis AI” which uses artificial
intelligence (AI) to predict EI mass spectra from chemical structures. We have used our newly-developed AI model to create adatabase of predicted EI mass spectra for around 100 million compounds. In this work, we introduce a polymer materials applicationthat uses msFineAnalysis AI for structural analysis.

AI Structural Analysis

y07.50O79106O115
131
100

79

1000Experimental

A commercially-available vinyl acetate resin was used as a test sample
in this study. We performed Py-GC-HRTOFMS measurements using both
EI and field ionization (FI) modes with a combination EI/FI ion source.
The qualitative data processing was performed with msFineAnalysis AI(JEOL). Measurement conditions are shown in Table 1.

m/z

50

50

167.07198.09100m/z150

200

Measured EI mass
spectrum of theunknown compounds

79 95

151
153

100EI/FI combination ion sourceIonization

EI+:70eV, 300μA
FI+:-10kV, 6mA/10msec (Carbotec)(Agilent Technologies)

Mass Range

DB-5msUI (Agilent)

Data processing condition

15m x 0.25mm, 0.25μm

SoftwaremsFineAnalysis AI (JEOL Ltd.)

50oC(1min)-30oC/min

Library database

NIST20, AI Library (JEOL Ltd.)

Carrier flow

He:1.5mL/min

O

50

198
200

027

O

O

79
107

59

91

138
166

123 152

100m/z

198
200

O
198

O

Figure 1: Workflow for structural
analysis of unknowns using
msFineAnalysis

Ion Source

Split mode (100:1)

150

OO

Finally, we got structural formulaof the unknown compounds!

Pyrolysis Temperature

Injection ModeO

139

139

JMS-T200GC (JEOL Ltd.)-330oC(1.7min)

O154

111

100m/z50
100
150200AI Predicted Spectrum (CID: 23339571), AI Score 778

Spectrometer

Oven Temperature

……

O

139

600oC

Column

122
94

77

O

166

150

123

39 51 67

EGA/PY-2020D(Frontier Lab)7890 GC

67

53

100

O

27 39 51 65 79 93 107 125

Pyrolyzer

Gas Chromatograph

y50

224

200m/zyO

O

MS conditions

GC conditions

41
270O

138

Table 1: Measurement and analysis conditions
Pyrolysis conditions

100165 192

Spectrum: EI (ID: 086, RT: 14.49 min)2.50

123.04

ONarrow down the predicted mass spectra
matched with chemical composition

0100

O

100

216
200

107

5950

10095.05

171m/zy139O

26 53 79 97 121

100 million compounds
predicted EI mass spectra

139.08

59.01

0150

O

27 55 810Mass Spectrum: EI (ID: 089, RT: 14.76 min)

50

50

182

O

50

5.00

0.00

151

100m/zO

100

C10H14O4x10 4O

50

Trained deeplearning model

Chemical composition of
the unknown compounds
determined by the softionization and GC-TOFMS

y100

4350

AIyO100

Intensity

The AI structural analysis workflow is shown in Figure 1. In this method,
we used deep learning to construct an AI model that can predict the EI
mass spectrum from a structural formula. We then submitted
approximately 100 million compound structure formulas to our AI model inorder to generate predicted EI mass spectra. The structural formula andthe predicted EI mass spectra associated with each compound areincluded with the software as an “AI library” database that also includes
database search function based on the mass spectral pattern. Additionally,
msFineAnalysis AI uses the molecular formulas uniquely determined
during automatic integrated qualitative analysis in order to narrow down
the possible candidate structural formulas.
The predicted EI mass spectrum narrowed down by molecular formulaand the actual EI mass spectrum are used to then calculate a score fromthe similarity of their spectral pattern, and the candidate structural
formulas are then listed in order of high similarity to low similarity.

m/z 35-800

Results and Discussion
Figure 2 shows the TIC chromatograms obtained from the Py-GC-EI and FI measurements. Diethylene Glycol Dibenzoate used
as a plasticizer was detected at RT 8.45 min (ID [134]) . In addition, compounds presumed to be pyrolysis products of Diethylene
Glycol Dibenzoate were detected in the RT 5.02 min (ID [085]), RT 5.98 min (ID [108]) and RT 6.98 min (ID [127]). All of thesecompounds were not registered in the NIST library database. Therefore, we performed AI structural analysis on these threecomponents and compared them with the structural formulas of pyrolysis products expected to be generated from Diethylene
Glycol Dibenzoate.
The measured EI mass spectra of these three components (top, black) and the predicted structural formula (right side of thespectrum) and its predicted EI mass spectrum (bottom, red) are shown in Figure 3. Regarding Diethylene Glycol Dibenzoate, theEI mass spectrum registered in the NIST library database is displayed in blue at the bottom, not the predicted EI mass spectrum.

GC/EI

GC/FI

Figure 2: Py-GC-EI and FI TIC chromatograms for Poly(vinyl acetate)［134］ Diethylene Glycol Dibenzoate［127］Spectrum: EI (ID: 127, RT: 6.98 min)

Mass Spectrum: EI (ID: 134, RT: 8.45 min)
105
105

100

07738 62 88

15 31 511188811877100105224162 192 224
179

OO0O

OO

43

5859149122
162194 217

179

121

77OOO223 254

OOH

149

105

［085］
Spectrum: EI (ID: 085, RT: 5.02 min)Spectrum: EI (ID: 108, RT: 5.98 min)
105

100

100

2777

100
200
AI Predicted Spectrum (CID: 89963), AI) Score...

［108］

045

100

149

0100
200
L01, Similarity: 905, Library Name: Diethylene...

45

105

100

149
149

65

27 43 57887791

122 149 169 194

121 149

179

210

O

0O
OH

105
100
200
AI Predicted Spectrum (CID: 88603), AI Score...

105100

O

77100

149

77
4270

29 43 6389132121149

O

192

77
105
100
200
AI Predicted Spectrum (CID: 21930739), AI Sc...

Figure 3: Measured EI mass spectra and predicted EI mass spectra of the proposed structural
formula for ID[085], [108], [127], [134] in Figure 2OO

AI structural analysis results are shown in Table 2. In the table, the “AI Score” is a score (up to 999) calculated by
msFineAnalysis AI that represents the cosine similarity between the measured and predicted EI mass spectra. "Rank" indicatesthe score rank of the structural formulas listed in Figure 3, and "Total" indicates the number of candidate structural formulas. All
three of the components analyzed in this study obtained a score of 850 or higher, indicating a high degree of similarity, and thefragment ions observed in the measured mass spectra and the predicted mass spectra were in good agreement. The number ofcandidate structural formula all exceeded 2,000 for each of the components, the presumed structural formula was obtained as thefirst or second candidate for all three components.

Table 2: AI structual analysis result
msFineAnalysis AI resultID

RT(min) IUPAC name

PubChem CIDAI Score Rank

Total

085

5.02

2-Ethenoxyethyl benzoate

21930739

858

14600

108

5.98

2-(2-Hydroxyethoxy)ethyl benzoate

88603

857

24544

127

6.98

2-[2-(2-Hydroxyethoxy)ethoxy]ethyl
benzoate

89963

872

12433

Conclusion
In this MSTips, we introduced our newly-developed software msFineAnalysis AI, which contains AI structural analysis
functionality to enhance qualitative analysis workflow. Additionally, the structural analysis application of additives and related
compounds in vinyl acetate using msFineAnalysis AI was also presented.
Structural analysis using AI was performed on three components not registered in the NIST library database, and results werecompared with the structures of pyrolysis products expected from Diethylene Glycol Dibenzoate. In spectral pattern comparisons,all cosine similarity scores were over 850, indicating that AI-predicted mass spectra showed a high degree of similarity tomeasured mass spectra. Even though the number of candidate structural formula exceeded 2,000 for each of the components,
the presumed structural formula was obtained as the first or second candidate for all three components. The prediction by AI
showed high accuracy, indicating that the method is effective for structural analysis of pyrolysis products and additives.
Qualitative analysis of GC-MS data can be greatly assisted by using EI and SI data together with msFineAnalysis AI, especiallywhen trying to identify unknown compounds in complex samples.