MSMSTips No. 389
GC-TOFMS Application

Structural Analysis of Acrylic Resin Oligomers
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, softionization (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 artificialintelligence (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

y07.50O79106O115
131100791000Experimental

A commercially-available acrylic resin was used as a test sample in thisstudy. 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

167.07
150

50

100

198.09m/z

150

200

Measured EI mass
spectrum of theunknown compounds

151
153

100

Ion Source

EI/FI combination ion source

Ionization

EI+:70eV, 300μA
FI+:-10kV, 40mA/30msec(Agilent Technologies)

Mass Range

ZB-5MSi (Phenomenex)

Data Processing Conditions

30m x 0.25mm, 0.25μm

SoftwaremsFineAnalysis AI (JEOL Ltd.)

40oC(2min)-10oC/min

Library database

NIST20, AI Library (JEOL Ltd.)

Split mode (100:1)

Carrier flow

He:1.0mL/min

OOO

150

79

O

166

50

198
200

027

107

59

91

138
166

123 152

100m/z

198
200

O
198

O

Figure 1: Workflow for structural
analysis of unknowns using
msFineAnalysis

Pyrolysis Temperature

Injection Mode……

O

139

OO

Finally, we got structural formulaof the unknown compounds!

JMS-T2000GC (JEOL Ltd.)-320oC(15min)

O

154

111

100m/z139

Spectrometer

Oven Temperature

122
94

7750
100
150200AI Predicted Spectrum (CID: 23339571), AI Score 778

600oC

Column67

53

O13979 95

12339 51 67

EGA/PY-3030D(Frontier Lab)

8890 GC

y50

100

O

27 39 51 65 79 93 107 125

Pyrolyzer

Gas Chromatograph

41
270

224

200m/zyOO

MS Conditions

GC Conditions

50O138

Table 1: Measurement and analysis conditions
Pyrolysis Conditions

100

165 192

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

59

50

10095.05

171m/zy139O

26 53 79 97 121

100 million compounds
predicted EI mass spectra

139.08

59.01

0150O

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 4O50

Trained deeplearning model

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

y100

4350

AIyO

100

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 downthe 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
Comparison of AI structural analysis results and references
Among the observed acrylic resin 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 [038], [040], [055], and [063] in Figure 2
are the four components analyzed in this study. Figure 3 shows the measured EI mass spectra for these four components (upper,black), the structural formula proposed in the reference literature (right side of the spectrum), and its predicted EI mass spectrum(lower, red).

GC/EI

GC/FI

Figure 2: Py-GC-EI and FI TIC chromatograms for an Methyl methacrylate-methyl acrylate
copolymer

ID [038]

ID [040]

Spectrum: EI (ID: 038, RT: 7.41 min)
97

100
0Spectrum: EI (ID: 040, RT: 8.32 min)

39
29

55

100

109 124 141 156

81O

109 123 141 156

097

39100

55

53
59 72

97

67

95

53

109 125 140
O

109

81

50
100150AI Predicted Spectrum (CID: 13426355,) 71327...

ID [055]

ID [063]100
4104153

83

59

85

67

Spectrum: EI (ID: 063, RT: 11.69 min)

1266795 111

126

81

100

139

186

154

O125 140

50100150
AI Predicted Spectrum (CID: 12512240), AI Sc...

Spectrum: EI (ID: 055, RT: 11.04 min)10039O736941

79100

186

113

50100150200
AI Predicted Spectrum (CID: 12037869), AI Sc...

O41O0OO

1002741

5355

6969

93
81

140

109

99

125
124

153
141200
168

O

O

200

50
100150200
AI Predicted Spectrum (CID: 10035672), AI Sc...

Figure 3: Measured EI mass spectra (upper, black) and predicted EI mass spectra (lower, red) of the
proposed structural formula in reference [1] for ID[038], [040], [055], [063] 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 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. Allfour of the components analyzed in this study obtained a score of 750 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 formulae all exceeded 3,000, but in three of the four components, structural formulas proposed in thereference literature were obtained within the top 1% of the candidates.

Table 2: AI structural analysis results
Reference [1] data
Notation Assignment of Main Peaks

msFineAnalysis AI resultID

RT(min) IUPAC name

PubChem CID

AI Score Rank

Total

d2

C=C(C)-C-C(C)(COOC)-C ?

038

7.41

Methyl 2,2,4trimethylpent-4-enoate

12512240

87225548

d4

C=C(C)-C=C(COOC)-C ?

040

8.32

Methyl 2,4-dimethylpenta71327190
2,4-dienoate

865

183769A2'

C=C(COOC)-C-C(COOC)-C ?

055

11.04

Dimethyl 2-methyl-412037869
methylidenepentanedioate

753373109

D1

C=C(COOC)-C-C(C)(COOC)-C ?

063

11.69

Dimethyl 2,2-dimethyl-410035672
methylidenepentanedioate

825

93732

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 acrylic resin 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, all cosine similarity scores
were over 750, indicating that AI-predicted mass spectra showed a high degree of similarity to measured mass spectra. Even
though the number of candidate structural formulae exceeded 3,000 for each of the components, the structural formulae proposedin the reference literature for three of the four components was in the top 1% of candidates. The prediction by AI showed highaccuracy, 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