msFineAnalysis AI Novel Qualitative
Analysis Software for JMS-T2000GC
with AI Structural Analysis
Ayumi Kubo

MS Business Unit, JEOL Ltd.

JEOL developed msFineAnalysis as qualitative analysis software for our gas chromatograph time of flightmass spectrometer (GC-TOFMS). We implemented deconvolution detection, variance component analysis, andother features in the software through updates. We have recently developed a new version of the series called
msFineAnalysis AI. msFineAnalysis AI is equipped with a structural analysis method using artificial intelligence(AI), called “AI structural analysis.” AI structural analysis enables the identiﬁcation of molecular formulas as well
as structural formulas of compounds that are not registered in the NIST 20 library (unknown compounds). The
workﬂow of AI structural analysis is as stated below.
First, msFineAnalysis’s integrated analysis function identiﬁes the molecular formula of an unknown compound.
Next, based on the identiﬁed molecular formula, structural formula candidates are extracted from PubChem, thedatabase containing over 100 million compounds. The AI predicts electron ionization (EI) mass spectra from theextracted structural formula candidates. Then, the structural formula candidates are ranked by comparing thepredicted mass spectra with the measured mass spectrum. Finally, a candidate that ranks ﬁrst is adopted as theanalysis result.
Using the NIST 20 library, we trained the AI to predict mass spectra from structural formulas and evaluated its
accuracy. From the results of accuracy evaluation, we conﬁrmed that AI structural analysis is useful in the structural
analysis of unknown compounds. In this report, we will introduce features of msFineAnalysis AI and provide ourevaluation results.

Introduction
The electron ionization (EI) method is widely used as an
ionization method for gas chromatograph mass spectrometry(GC-MS). Fragment ions are mainly observed in a mass
spectrum obtained by the EI method (herein, an EI mass
spectrum). Fragment ions reflect the structure of a compoundand has a pattern unique to it. For this reason, in qualitative
analysis of GC-MS, an EI mass spectrum is compared withlibraries of EI mass spectra of reference compounds. The NIST
library, the most widely used library of structural formulas andmass spectra, has about 300,000 registered compounds.
Meanwhile, PubChem, a major compound database,contains over 100 million substances as of 2023. However, EI
mass spectra are not registered in PubChem. This means thatmost compounds in PubChem do not have EI mass spectral
information, except for some also registered in the NIST library.
When library searches are performed for EI mass spectra of suchcompounds, qualitative analysis results may not be obtained,
or wrong compounds may possibly be identified. For thesecompounds that are not registered in the NIST 20 library, it isuseful to combine [2] the field ionization (FI) and other soft〉〉3-1-2 Musashino, Akishima, Tokyo 196-8558, Japan | E-mail: akubo@jeol.co.jp

53

JEOL NEWS │ Vol.58 No.1 (2023)

ionization methods with a mass spectrometer [1] that obtains
DFFXUDWHPDVV7KHVSHFL¿FSURFHGXUHLVDVIROORZV
1. The EI and soft ionization mass spectra are compared, and amolecular ion peak is determined.
2. Based on the accurate mass of the determined peak, molecular
formula candidates are obtained.
3. For obtained molecular formula candidates, isotope patternanalysis and accurate mass analysis of fragment ions in the EI
mass spectrum are performed. Based on the results of these
two analyses, the molecular formula is determined.
The above method is implemented in msFineAnalysis, which
HQDEOHVWKHDXWRPDWHGLGHQWL¿FDWLRQRIWKHPROHFXODUIRUPXODRI
an unknown compound. We have newly developed a structural
analysis method using artificial intelligence (AI), called “AI
structural analysis,” with an aim to obtain not only molecular
formulas but also structural formulas of unknown compounds.
The new version of msFineAnalysis equipped with AI structural
analysis, msFineAnalysis AI, was introduced to the market in
January 2023. In this article, we will provide an overview of AI
structural analysis and report the results of its accuracy evaluation.
In addition, we will show the results of applying this function tocompounds that are not registered in the NIST 20 library.

JEOL NEWS │ Vol.58 No.1 (2023)

AI structural analysis$, VWUXFWXUDO DQDO\VLV XVHV WZR W\SHV RI $, PDLQ $, DQGsupport AI. Figure 1 shows the procedures of integrated analysis
and AI structural analysis for compounds that are not registeredin the library. msFineAnalysis AI automatically performs thedetection of a compound and steps 1 to 4 below. Details about
two types of AI are described in the next section.
1. msFineAnalysis AI performs integrated analysis using the
EI mass spectrum and the mass spectrum obtained by the
FI method, a soft ionization method, to identify a molecular
formula.
2. Based on the identified molecular formula, the software

extracts structural formula candidates from PubChem databasethat contains over 100 million compounds. Ten thousand orless candidates are extracted.
3. The main AI predicts EI mass spectra for the extracted
structural formula candidates.
4. By comparing the predicted EI mass spectra with the actual
measured EI mass spectrum, the software ranks the structural
formula candidates using AI scores (cosine similarities).
)LQDOO\WKHFDQGLGDWHWKDWUDQNV¿UVWLVDGRSWHGDVWKHDQDO\VLV
result.
*The software displays the structural analysis results obtained
through steps 1 to 4, as well as accurate mass information and

Fig. 1 Overview of AI structural analysis.

峆峧岼峕峸孽季守完季孫完宇孽季孳孹孵孯季宕宗孽季孴孴孱孹孻季宰宬宱孬
宻孴孳 學

孵孱孳孳

EI mass spectrum (measured)
‫ౕک‬

孴孱學孳

1. Integrated
analysis孴孱孳孳Formula: C10H16O4

孳孱學孳

孳孱孳孳
孷孳

學孳

孹孳

孺孳

孻孳

孼孳

孴孳孳

孴孴孳

孴孵孳

孴孶孳

孴孷孳

孴學孳

孴孹孳

孴孺孳

孴孻孳

孴孼孳

孵孳孳

宰孲宽
峆峧岼峕峸孽季宖完季孫完宇孽季孳孹孵孯季宕宗孽季孴孴孱孹孻季宰宬宱孬
宻孴孳 孹

孴孱孳孳

2. Search the compound databaseusing the molecular formula完宐孵孳孳孱孴孳孷孼孺FI mass spectrum (measured)

‫ౕک‬

孳孱孺學

孳孱學孳

孳孱孵學

孳孱孳孳
孷孳

學孳

孹孳

孺孳

孻孳

孼孳

孴孳孳

孴孴孳

孴孵孳

孴孶孳

孴孷孳

孴學孳

孴孹孳

孴孺孳

孴孻孳

孴孼孳

孵孳孳

宰孲宽

6XSSRUW$,m/z

Fragment

DBE

Description

Neutral Loss

DBE

Description

41.03866 C3 H5

1.5 Allyl-C7 H11 O4

2.5

59.01280 C2 H3 O2

1.5 Ester-C8 H13 O2

2.5

69.03361 C4 H5 O

2.5 Methylcyclopentanone, Cycloh -C6 H11 O3

1.5

73.06486 C4 H9 O

0.5 Alcohol, Ether-C6 H7 O3-C4 H9 O4

Structure candidates for C10H16O4

0DLQ $,

3.5

79.05423 C6 H7

3.5 Aromatic

81.06989 C6 H9

2.5 Cyclohexane, Cyclohexenyl es -C4 H7 O4

1.5

101.05980 C5 H9 O2

1.5 Dimethyl acetal-C5 H7 O2

2.5

109.06482 C7 H9 O

3.5

-C3 H7 O3

0.5

125.05978 C7 H9 O2

3.5 Ethylene acetal-C3 H7 O2

0.5

0.5

140.08339 C8 H12 O2

3.0-C2 H4 O2

1.0 Acetate, Methyl ester- C10 H16 O4

3.0

-

--

Accurate mass information

Prediction results ofpresence/absence of substructures

3. The main AI predicts the EI mass spectrum
from the structural formula

Comparison

EI mass spectrum (predicted)

孴孳孳
學孳
孳

學孳

孺學

孴孳孳

孴孵學

孴學孳

孴孺學

孵孳孳

EI mass spectrum (predicted)

孴孳孳

學孳
孳
學孳

孺學

孴孳孳

孴孵學

孴學孳

孴孺學

孵孳孳

EI mass spectrum (predicted)

孴孳孳

學孳
孳

學孳

Structure analysis result

孺學

孴孳孳

孴孵學

孴學孳

孴孺學

孵孳孳

AI library
4. Determine the structural formula by calculating thecosine similarity (AI score) of the EI mass spectra of
the measured and predicted54the results of partial structure prediction by the support AI.
Analysts can use this information and knowledge to interpret thestructural analysis results. However, this process is performed
independently, and the structural analysis results can be
automatically obtained without it.
Features of AI structural analysis include the EI mass
spectrum prediction by main AI, as well as narrowing down
candidates based on a molecular formula identified withintegrated analysis. Before the measured mass spectrum iscompared with AI-predicted EI mass spectra, the molecular
formula identified by integrated analysis helps narrow down
structural formula candidates. This allows the scope of structural
formula candidates to be narrowed from 100 million to 10,000
or less, making it possible to perform an efficient and highly
accurate structural analysis.
If a molecular formula is not identified in advance, themeasured EI mass spectrum must be compared against the entire
compound database, or must be narrowed down using compound
species. In comparison against the entire database, the measured
spectrum must be compared with 100 million EI mass spectra,
resulting in a time-consuming and less accurate analysis.
The reason for a lower accuracy is that some compounds are
difficult to distinguish from others based on EI mass spectral
information alone. The four compounds shown in Fig. 2 havedifferent structural and molecular formulas, but exhibit highly
similar EI mass spectra. Therefore, only comparing their EI
mass spectra is not sufficient for identification and may leadto wrong qualitative analysis results. Meanwhile, to identify
compound species, information about samples and analysts’
experience and knowledge are required. If there is not enoughsample information, identifying compound species will be
GLI¿FXOW$GGLWLRQDOO\DQLQFRUUHFWVHOHFWLRQRIVSHFLHVFDQOHDG
to wrong structural analysis results. Consequently, analysis
might be dependent on individual skills of analysts, resulting ina low reproducibility. On the other hand, AI structural analysis

generates correct analysis results for the four compounds shownin Fig. 2, because it narrows down structural formula candidates
EHIRUHKDQGXVLQJWKHPROHFXODUIRUPXODLGHQWL¿HGE\LQWHJUDWHG
analysis as mentioned earlier.
msFineAnalysis AI is not equipped with the main AI. Instead,
it is equipped with the “AI library,” which contains structural
formulas extracted from PubChem and mass spectra predictedfrom the structural formulas by the main AI. The AI libraryhelps eliminate the need for mass spectrum prediction during
analysis, improving the analysis throughput. After an analyst
selects measurement data and presses the button to start theanalysis, msFineAnalysis AI automatically performs all theprocessing to complete the structural analysis. The analyst canobtain structural analysis results for 100 compounds within 10
minutes. The AI library also eliminates the need for connecting
to the compound database via the Internet during analysis,
enabling a stable and stand-alone analysis.
Figure 3 shows the graphical user interface (GUI) of AI
structural analysis. Structural formulas are listed in descendingorder of AI score at the lower part of the window. On the top
left corner of the list is the structural analysis result. As theinformation about the structural formula, its IUPAC name and3XE&KHP&,' LGHQWL¿FDWLRQQXPEHULQ3XE&KHPGDWDEDVH DUH
also displayed. The number of structural formula candidates forthe molecular formula and the histogram created using AI scoreare displayed on the upper right of the window. These various
kinds of information help the analyst see the whole picture of
the structural analysis results.
In addition, if there is knowledge about the target compound,
WKHDQDO\VWFDQ¿OWHUVWUXFWXUDOIRUPXODVXVLQJSDUWLDOVWUXFWXUHVsuch as benzene ring and methyl ester. When the analyst presses
the button on the right edge of the window, it displays the mass
spectrum and information for accurate mass as well as theprediction results of partial structures performed by the support$,7KHDQDO\VWFDQFRQ¿UPDQGLQWHUSUHWWKHVWUXFWXUDODQDO\VLV
results.

Fig. 2 EI mass spectra of four compounds registered in the NIST 20 library.

D 'LHWK\OHQHJO\FROGLEHQ]RDWH>&+2@E %HQ]RLFDFLG QLWURSKHQ\O HWK\OHVWHU>&+12@

5HODWLYH,QWHQVLW\

5HODWLYH,QWHQVLW\


























ƐŒƝ

F   (WKDQHGL\OELV R[\ ELV HWKDQHGL\O GLEHQ]RDWH>&+2@







G %HQ]RLFDFLG FKORURSKHQR[\ HWK\OHVWHU>&+&O2@
5HODWLYH,QWHQVLW\

5HODWLYH,QWHQVLW\













ƐŒƝ

55


ƐŒƝ

JEOL NEWS │ Vol.58 No.1 (2023)














ƐŒƝ







JEOL NEWS │ Vol.58 No.1 (2023)

Two types of AI
This section describes two types of AI used in AI structural
analysis.
The main AI employs Graph Convolutional Networks (GCN)[3], a type of deep learning, as its model (Fig. 4, top). GCN
RSHUDWHV DV IROORZV )LUVW WKH PDFKLQH VHDUFKHV VWUXFWXUDO
formulas for partial structures that produce signals characteristicof a mass spectrum, and generates a lot of partial structures.
Then, the machine predicts a mass spectrum based on thegenerated partial structural information (Fig. 4, bottom).
7KH VSHFLILF SURFHVVLQJ LV DV IROORZV )LUVW WKH VWUXFWXUDO
formula is converted to graph data before being input into GCN(Fig. 5). In graph data, atoms and bonds in the structural formula
are treated as nodes and edges, respectively. In addition, nodes
hold information on the elemental species of atoms, and edges
hold information on the type of bonds, as their feature vector.
For example, a node for the carbon atom has the feature vector(1, 0, 0, ...), a node for the oxygen atom has the feature vector(0, 1, 0, ...), and a node for the nitrogen atom has the feature
vector (0, 0, 1, ...).
Next, the machine performs convolutions on the structural
formula that was converted to graph data as shown in the top
left of Fig. 4. Through convolutions, each node sifts throughand obtains information on neighboring nodes and edges. The
machine learns to recognize the connection of atoms as a blockby repeating convolutions.

Then, the machine performs pooling of each atom as shownin the top right of Fig. 4. This enables the machine to grasp
the characteristics of the structural formula and predict a mass
spectrum.
The support AI employs the traditional machine learning(regression) instead of deep learning. The machine predicts thepresence or absence of 48 partial structures from ions and neutral
loss based on the accurate-mass mass spectra (Fig. 6). The
VXSSRUW$,LVVLPSOHDQGXVHVGR]HQVRIFRHI¿FLHQWV7KHUHIRUH
the machine can provide prediction results and their characteristic
peaks at the same time.

Accuracy evaluation of AI structural
analysisʊ Accuracy evaluation of EI mass spectrum prediction ʊ

AI structural analysis uses mass spectra that are predictedfrom the structural formulas by the main AI. The main AI
was trained using the structural formulas and mass spectra of
270,000 compounds, which account for 90% of the NIST 20
library data. During training, the weight of the main AI was
optimized so that patterns of mass spectra predicted from thestructural formulas match those of mass spectra in the NIST 20
library. Out of the remaining 30,000 compounds, 10,000 were
DOORFDWHGIRUYDOLGDWLRQWRSUHYHQWRYHU¿WWLQJDQGZHUH
used to evaluate the accuracy of EI mass spectrum prediction.
We evaluated the accuracy of the main AI’s EI mass spectrum

Fig. 3 GUI of AI structural analysis.
Comparison of measured and predicted EI mass spectra
Histogram for AI score

Details of
selected
structure

Structural formulas ordered by descending AI score
Filtering by substructure

Determined
structure

Display result of support AI

Accurate mass information

Prediction results of presence/absenceof substructures56

Fig. 4 Graph Convolutional Networks used in the main AI.

3RROLQJ&RQYROXWLRQ

Predict from substructure information

Generate substructures
2

&
&

&

&

&

2

2

&

&

&

&

2

&

&

2

&

&

&

&

2
&

&
&

&

&

2

&

&

&

&

&

2

&

&
&

&

&

&

&

2

&

2

&

&

&

&
&

&

&

&

&

2

2

&

&

2

&

&

2

&

&

&

&

&

2

&

&

2

&

&

&

&

&

2

&

&

&

&

&

&

&

2

&

2

&

&

&

&

&

&

&
2

57

&

JEOL NEWS │ Vol.58 No.1 (2023)

&
&

1

&

&

&

&

&

&

&

&

&

&

&

&

&

&

&

&

&

2

&

&

&

&

2

&

&

&

&

2

&
&

&

&

Fig. 5 Conversion of a structural formula to graph data.

2

2

&
2

&

&

&

&

2

&

2

&

&

&

&

2

&

2

&

&

&

&

&

&

&

2

&

&

&

&

&

&
2

2

&

&

&

&
&

&

Structure(graph data)

&

2

&
2

&

&

&
&

2

&

&

Predicted EI mass spectrum

JEOL NEWS │ Vol.58 No.1 (2023)

prediction using 20,000 compounds that were not used intraining. In the evaluation, the trained main AI predicted EI mass
spectra from the structural formulas of the target compounds.
We used the cosine similarity between the predicted EI mass
spectrum and an EI mass spectrum registered in the NIST 20
library as the index of accuracy evaluation. A cosine similarityof 1 means the two EI mass spectra match perfectly. As the
cosine similarity is closer to 0, they match less.
Figure 7 shows a histogram of cosine similarities calculatedusing 20,000 compounds. The histogram shows that more than
90% of the compounds had a cosine similarity of over 0.4.
In addition, the 0.7-0.8 segment had the highest number ofthe compounds. The average cosine similarity was 0.72. We
FRQ¿UPHGWKDWWKHPDLQ$,FDQUHSURGXFHPDVVVSHFWUDZLWKD
high accuracy by predicting them from the structural formulas.
Figure 8 shows as examples the comparison between themeasured and predicted EI mass spectra for each of thecompounds with above-average, near-average, and belowaverage cosine similarities. For Benzamide, 3-methyl-N-decyl-,which had an above-average cosine similarity, the EI mass

spectrum was reproduced almost completely including mass
peaks with low intensity. The reason is thought to be that thiscompound consists of only benzene rings, alkane chains, andamide groups, many of which are registered in the NIST 20
library. For N-Acetyl-3-(3-formyl-4-methoxyphenyl)-d-alanine
methyl ester, which had a near-average cosine similarity,
mass peaks with relatively high intensity were reproduced,
and the overall patterns were similar. This compound hasa somewhat complex structure, with multiple side chainsattached to a benzene ring, compared with the structural
formula of Benzamide, 3-methyl-N-decyl-. This is thought
to be why a complete mass spectrum was not reproduced.
For Cyclododecane, 1,5,9-tris(acetoxy)-, which had a belowaverage cosine similarity, the overall pattern was not well
reproduced. A possible reason is that this compound includes
a large 12-membered ring, and the NIST 20 library contains asmall number of compounds that have this ring. This may have
prevented the machine to be trained enough. However, somemass peaks, including the most intense one at m/z 43, werereproduced.

Fig. 6 Overview of the support AI.

&+2&+2

&+

&+2

LVSUHVHQW

&+2

LVDEVHQW

0&+2&+2

2
LVSUHVHQW

2+

Fig. 7 Histogram of cosine similarities calculated using 20,000 compounds that were notused in training.

Number of Compounds

3000

2000

1000

0
0

0.2

0.4

0.6

0.8

1.0

Cosine Similarity

58

Fig. 8 Comparison between the measured and predicted EI mass spectra.

a) benzamide, 3-methyl-N-decyl[cosine similarity: 0.95]


5HODWLYH ,QWHQVLW\

MeasuredPredicted














ƐŒƝ

b) N-Acetyl-3-(3-formyl-4-methoxyphenyl)-d-alanine methyl ester[cosine similarity: 0.72]


5HODWLYH ,QWHQVLW\

MeasuredPredicted













ƐŒƝ

c) cyclododecane, 1,5,9-tris(acetoxy)[cosine similarity: 0.34]


5HODWLYH ,QWHQVLW\

MeasuredPredicted











ƐŒƝ

59

JEOL NEWS │ Vol.58 No.1 (2023)







JEOL NEWS │ Vol.58 No.1 (2023)

ʊ Accuracy evaluation of structural analysis ʊ

AI structural analysis compares EI mass spectra predictedfrom structural formula candidates with the actual measured EI
mass spectrum to identify the structural formula. We evaluatedthe accuracy of this structural formula identification. The
HYDOXDWLRQ PHWKRG LV DV IROORZV )LUVW IRU WKH FRPSRXQGV LQ
the NIST 20 library that were not used in training, structural
formulas (compounds) that have the same molecular formulawere extracted from the compound database. Next, the trained
main AI predicted EI mass spectra for the correct structural
formula and the extracted ones. The predicted EI mass spectra
were compared with the ones registered in the NIST 20 library,
and based on their cosine similarities, all the structural formulas,
including the correct one, were ranked. We used the rank
given by the correct structural formula among all the structural
formulas as the index of accuracy evaluation. In this evaluation,
to set certain criteria, we used only molecular formulas forwhich at least 100 compound candidates were extracted from thecompound database.
Table 1 shows the results of ranking structural formulasfor 14,581 compounds. The results indicate that the correct
structural formula ranked top for 22% of the compounds. In
addition, the correct structural formula ranked in the top 1% for
73% of the compounds. Ranking in the top 1% means that thecorrect structural formula was placed within the top 10 out of
1,000 candidates. The PubChem compound database contains
many compounds that have quite similar structural formulas.
With taking this into consideration, this structural formula
LGHQWL¿FDWLRQPHWKRGLVVDLGWREHKLJKO\DFFXUDWH
Next, we evaluated the effectiveness of this method forcompletely unknown compounds. We used model compounds thatare not registered in the NIST 20 library to perform the evaluation.
7KHIROORZLQJPRGHOFRPSRXQGVZHUHXVHG&DIHQVWUROH &$6
 :DNR  0&3$WKLRHWK\O &$6 
:DNR 3URSDSKRV &$6:DNR &13DPLQR &$6
:DNR %XWDPLIRVR[RQ &$6:DNR 
DQG,VR[DGLIHQHWK\O &$6:DNR 
The measured EI mass spectra for the model compounds
were prepared by measuring standard samples. Table 2 shows

Table 1 Results of accuracy evaluation on
14,581 compounds.


Number ofCompoundsTop

Withinthe top 1٫

Withinthe top 5٫

Withinthe top 10٫







 ᧡  ᧡  ᧡  ᧡ 



Table 2 Examples of structural analysis results.
6LPLODULW\

5DQN&DIHQVWUROH&RPSRXQG1DPH

6WUXFWXUH 

0&3$WKLRHWK\O 

3URSDSKRV



 

&13DPLQR 

%XWDPLIRVR[RQ



 

,VR[DGLIHQHWK\O 

7RSVWUXFWXUHV

60

the rank given by the correct structural formula, its score,
and top 10 structural formulas in descending order of scorefor each model compound. For three compounds out of thesix, the correct structural formula ranked top. For Isoxadifenethyl, the correct structural formula ranked lowest compared
ZLWKWKHRWKHU¿YHPRGHOFRPSRXQGV+RZHYHULWZDVSODFHG
22nd out of 5,348 candidates, within top 1%. The result
suggests that this structural formula identification method iseffective in narrowing down the correct structural formulafrom many candidates. The top-ranked structural formulas for
Cafenstrole, CNP-amino, and Isoxadifen-ethyl have the same
size and number of rings as their correct structural formulasdo, and they show considerable similarity. The results ofour evaluation on these six compounds reveal that thisLGHQWL¿FDWLRQPHWKRGLVXVHIXOLQVWUXFWXUDODQDO\VLVFigure 9shows the comparisons between the measured and predictedmass spectra. The measured and predicted mass spectra exhibit
the same peaks with high intensity, although they are different
in detailed peak intensities and distributions of mild peaks.
These results confirm that this method is effective in the
structural analysis of unknown compounds.

Conclusions
Previous msFineAnalysis software features integrated
analysis based on accurate mass measurement and molecularion observation using the soft ionization method, which arefeatures of the JMS-T2000GC. Integrated analysis enables theidentification of molecular formulas of unknown compounds.
The new version, msFineAnalysis AI, is equipped withstructural analysis using artificial intelligence (AI), whichenables molecular formulas as well as structural formulas to be
automatically obtained. msFineAnalysis AI extracts structural
IRUPXODFDQGLGDWHVEDVHGRQWKHPROHFXODUIRUPXODVLGHQWL¿HG
by integrated analysis. Then, it uses the EI mass spectra
predicted from the structural formula candidates by the AI to
identify the structural formula. The combination of integrated
DQDO\VLVDQG$,HQDEOHVDKLJKO\HI¿FLHQWDQGDFFXUDWHVWUXFWXUDO
analysis. All the processes are performed automatically andRIÀLQHOHDGLQJWRDVWDEOHDQDO\VLV

References[ 1 ] Masaaki Ubukata. MultiAnalyzer – Unknown Compounds
Analysis System New Gas Chromatograph Time-of-Flight
Mass Spectrometer JMS-T2000GC “AccuTOF TM GCAlpha”. JEOL news Vol. 56.
[ 2 ] Masaaki Ubukata, Yoshihisa Ueda. Development of an
Integrated Analysis Method for the JMS-T200GC High
Mass-Resolution GC-TOFMS by Electron Ionization andSoft Ionization Methods. JEOL news Vol. 54.
[ 3 ] J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vynyals, G.
E. Dahl. Neural message passing for Quantum chemistry.
Proceedings of the 34th International Conference on
0DFKLQH/HDUQLQJ

Fig. 9 Comparison between the measured and predicted mass spectra.
a) Cafenstrole [cosine similarity: 0.741]b) MCPA-thioethyl [cosine similarity: 0.735]
Measured

Measured

Predicted

Predictedc) Propaphos [cosine similarity: 0.802]d) CNP-amino [cosine similarity: 0.710]
Measured

Measured

Predicted

Predictede) Butamifos oxon [cosine similarity: 0.675]

61

JEOL NEWS │ Vol.58 No.1 (2023)f) Isoxadifen-ethyl [cosine similarity: 0.586]
Measured

Measured

Predicted

Predicted