MSMSTips No. 391
GC-TOFMS Application

Structural Analysis of Polyethylene Terephthalate Filmby 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 mass a 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 a
database 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

y07.500100m/z

79198.09100

150

200

Measured EI mass
spectrum of theunknown compounds

79 95

151
153

100

O……

O

154

111

139

150

100m/z100OOO

79O

166

50

198
200

027

107

5991

138
166

123 152

100m/z

198
200

O
198

OOO

Finally, we got structural formulaof the unknown compounds!

Figure 1: Workflow for structural
analysis of unknowns using
msFineAnalysis

Ion Source

EI/FI combination ion source

Ionization

EI+:70 eV, 300 μA
FI+:-10 kV(Agilent Technologies)

Mass Range

ZB-5MSi (Phenomenex)

Data processing condition

30 m x 0.25 mm, 0.25 μmSoftware

msFineAnalysis AI (JEOL Ltd.)

40oC (2 min) - 20oC/min

Library database

NIST20, AI Library (JEOL Ltd.)

He:1.0 mL/min

122
94

77139

Pyrolysis Temperature

Carrier flow

67

53O50
100
150200AI Predicted Spectrum (CID: 23339571), AI Score 778

JMS-T200GC (JEOL Ltd.)

Split mode (100:1)

y50

224

200m/z139

Spectrometer

Injection Modem/z

150

123

39 51 67

600oC

-320oC (30 min)

41
270O

27 39 51 65 79 93 107 125

EGA/PY-2020D(Frontier Lab)

Oven Temperature

50OO50PyrolyzerColumn

100

165 192yO

107138MS conditions

7890 GCOSpectrum: EI (ID: 086, RT: 14.49 min)167.07123.04O100

216
200y139O

26 53 79 97 121

Narrow down the predicted mass spectra
matched with chemical composition

2.50

95.05

171m/z5950

Table 1: Measurement and analysis conditions

Gas Chromatograph

O

115
131
100

100

059.010100 million compounds
predicted EI mass spectra

139.08

50

50

182

150

O

100

0.00

151

100m/z27 55 810Mass Spectrum: EI (ID: 089, RT: 14.76 min)

A commercially-available polyethylene terephthalate film was used as atest 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 withmsFineAnalysis AI (JEOL). Measurement conditions are shown in Table 1.

GC conditions

106

O50

5.00

Experimental

Pyrolysis conditions

O79

O

100

C10H14O4x10 4O

50

Trained deeplearning model

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

y100

4350

AIyO100Intensity

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 29 - 600

Results and Discussion
Comparison of AI structural analysis results and references
Among the observed polyethylene terephthalate (PET) pyrolysis products, AI structural analysis was performed for four
components not registered in the NIST library database and for which structural formulas were proposed in reference [1]. Figure 2
shows the TIC chromatograms obtained from the Py-GC-EI and FI measurements. The peaks with IDs [058], [059], [115], and[159] in Figure 2 are the four components analyzed in this study. Figure 3 shows the measured EI mass spectra for these fourcomponents (upper, black), the structural formula proposed in the reference literature (right side of the spectrum), and its
predicted EI mass spectrum (lower, red).
TICC: EI Data

TICC: EIデータ

強度Intensityx10 7[059]
10.81

[058]
10.49

[115]
15.52
[159]
22.66

0.00

2.5

5.0

7.5

10.0

12.5

[058]
10.49

[059]
10.81

15.0

TICC: SI Data

TICC: SIデータ

x10 6
強度Intensity

GC/EI

5.00

5.00

RT [min]

17.5

20.0

22.5

25.0

27.5

30.0

GC/FI
[115]
15.52

[159]
22.66

0.00
2.5

5.0

7.5

10.0

12.5

15.0

RT [min]

17.5

20.0

22.5

25.0

27.5

30.0

Figure 2: Py-GC-EI and FI TIC chromatograms for Poly(ethylene terephthalate)

ID [058]

ID [059]
Spectrum: EI (ID: 059, RT: 10.81 min)Spectrum: EI (ID: 058, RT: 10.49 min)

175

100

02850 65

27 42

104
85

117

147

189 218
190 21865 91 119

OOO

O

0149

100

149

100

65

28 42

37 50 65

83 104161 181165 192

93

100

Spectrum: EI (ID: 159, RT: 22.66 min)

297

42

77

123

105

105

100
O

207 251

191 227 269

O

ID [159]

Spectrum: EI (ID: 115, RT: 15.52 min)

0

O

149

ID [115]105 149
28 63 89

O

50
100
150
200
AI Predicted Spectrum (CID: 22223159), AI...

100
200
AI Predicted Spectrum (CID: 15374889), AI...

100

HO

121

100

175

132

339

149
297

100
200300AI Predicted Spectrum (CID: 101115782), A ...

OOO

O

O0100

32

42297

77

149

77

149

O

207

341 399 462

OOO

199 255 323

OOOO

105
0250
AI Predicted Spectrum (CID: 53951693), AI...

Figure 3: Measured EI mass spectra and predicted EI mass spectra of the proposed structural formula inreference [1]for ID[058], [059], [115], [159] in Figure 2

AI structural analysis results are shown in Table 2. In the table, the “AI Score” is a score (up to 999) calculated bymsFineAnalysis 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.
Three of the four components analyzed in this study (ID [058], [059], [115]) obtained over 800 score which means a high degree ofsimilarity, and the fragment ions observed in the measured mass spectra and the predicted mass spectra were in good agreement.
The number of candidate structural formulas ware about 970-1400, and the structural formulas proposed in the reference wereobtained as within the top two.
For ID [159], the AI Score was calculated at 510 score, and although this score was lower than the other components, thestructural formula proposed in the reference literature was obtained at #3 rank in 311 candidates.
As a result, it was found that the structural formulas proposed in the reference literature were obtained in the top three positions
for all the components evaluated in this study.

Table 2: AI structual analysis result
Reference [1] data
Notation Assignment of Main Peaks
CCH2=CHOCOC6H4COOCH=CH2

msFineAnalysis AI result
ID058

RT(min)IUPAC name

PubChem CID

AI Score

Rank

Total

10.49

bis(ethenyl) benzene-1,4dicarboxylate

15374889

820

21471

914

21013

877

19755103311

D

CH2=CHOCOC6H4COOH

059

10.81

F

C6H5COOCH2CH2OCOC6H4COOCH=CH2115

15.52

H

C6H5COOCH2CH2OCOC6H4COOCH2CH2OCOC6H5159

22.66

4-ethenoxycarbonylbenzoic
22223159
acid
1-O -(2-benzoyloxyethyl) 4101115782
O -ethenyl benzene-1,4bis(2-benzoyloxyethyl)53951693
benzene-1,4-dicarboxylate

Conclusion
In this MSTips, we introduced our newly-developed software msFineAnalysis AI, which contains AI structural analysis
functionality to enhance qualitative analysis workflow. Additionally, a polymer application using msFineAnalysis AI to identify
components of a pyrolyzed PET film was also presented.
Structural analysis using AI was performed on four components not registered in the NIST library database, and results werecompared with structural formulae proposed in the reference literature.
In spectral pattern comparisons, Three of the four components analyzed in this study (ID [058], [059], [115]) cosine similarity
scores were over 800, indicating that AI-predicted mass spectra showed a high degree of similarity to measured mass spectra.
The number of candidate structural formulas ware about 970-1400, and the structural formulas proposed in the reference wereobtained as within the top two. For ID [159], the AI Score was calculated at 510 score, and although this score was lower than theother components, the structural formula proposed in the reference literature was obtained at #3 rank in 311 candidates. Theprediction by AI showed high accuracy, indicating that the method is effective for structural analysis of pyrolysis products.
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.

Reference［1］Shin Tsuge, Hajime Ohtani, Chuichi Watanabe (2011), Pyrolysis - GC/MS Data Book of Synthetic Polymers, Elsevier