MSMSTips No. 388
GC-TOFMS Application

Introduction of AI Structure Analysis Functionin Automatic Structure Analysis Software 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, weintroduced 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 AI structure analysis function
in automatic structure analysis software msFineAnalysis AI.

Figure 1 Image of analysis result in msFineAnalysis AIAbout AI Structure Analysis Function

AI structure analysis function performs automatic structure the analysis for unknown compounds using two AIs (main AI, support AI)
that complementarily combine machine learning and deep learning.
Figure 2 shows the workflow of AI structural analysis by the main AI. In the main AI, a model for EI mass spectra prediction fromstructural formulas was constructed using deep learning, and predicted EI mass spectra of 100 million compounds were included in
the software as an "AI library" database. The database search function using the "AI library" is implemented similarly to traditional
library searches using the commercially available EI mass spectra database. Structural formula candidates are narrowed down by
molecular formulas uniquely determined by integrated qualitative analysis, so more correct structural formulas can be obtained
quickly. The predicted EI mass spectra were compared with measured EI mass spectra, then the scores were calculated from thespectral patterns, and candidate structural formulas were arranged in order of highest score. Finally, the correct structural formula isselected by combining the obtained structural formula candidates with the sample information and the knowledge and know-how
obtained from the previous analysis.
Figure 3 shows the workflow of partial structure prediction by the support AI. The support AI assists interpreting analysis results by
predicting the partial structure from the measured EI mass spectrum. It is possible to analyze the composition formula of fragment
ions and neutral losses obtained from accurate mass analysis and assist in the interpretation of structural information proposed by themain AI.

AI library of 100 million compounds

79100

C10H14O4500Molecular formula determined byintegrated qualitative analysis

Intensity

x10 4

7.50

139.0895.05
100167.07

123.04m/z

150m/z

150OO

166

50

198
200

02759

79
10791

138
166
198

123 152

100m/z200

C8H12O2・・・
C8H14
C8H11O2
Compositional formula determined byintegrated analysis (Fragment ion and
Neutral Loss composition formula)

139

100

2.50

59.01

138OO

Narrow down the predicted mass spectra
matched with chemical composition

050

O

100

100O
O

C7H5O
C10H10O4

C6H5

C8H4O2-C3H5O3
M-C4H5O4-C2H4O2

OOH

Predicted presence or absence of thepartial structures

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

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

5.00

0.00

O

107

5950

Machine learning model for predicting partial structures

198.09200

Measured EI mass spectrumof the unknown compounds

10079 95

123

151

27 39 51 65 79 93 107 125

153

39 51 67

O

O
198

139

OO

O

O

O

O

50
100
150
200
AI Predicted Spectrum (CID: 23339571), AI Score 778

Finally, we got structural formulaof the unknown compounds!

Figure 2 Main AI workflow

Structural formula provided by Main
AI

Comparison of partial structure prediction
results with AI library structure formulas

Figure 3 Support AI workflow

GUI of AI Structure Analysis Result
Figure 4 shows the AI structure analysis result of Acrylic Resin Oligomers by msFineAnalysis AI. The target of analysis
is a dimer component that is not registered in the NIST library database. The left side of the analysis result screen shows
the structure candidates by the main AI, and the right side shows the analysis results by the support AI. Detailed
structural information can be obtained even for unknown compounds that have not been registered in the database.
On the main AI analysis result screen, a list of predicted structural formulas is shown at the bottom of the screen, and it
is possible to check the AI structural analysis results all at once. The AI score indicates the similarity between the AI
library and the measured mass spectrum , and it is shown at the bottom of each structural formula. Furthermore,
information on the selected structural formula is posted at the top of the screen. We can see where the selected structural
formula is in the histogram. It also includes a filtering function by partial structure and monomer, which enables structural
analysis results to reflect the presence or absence of substructures predicted by the support AI is described below.
On the support AI analysis result screen, predicted partial structure information is shown at the bottom of the screen. On
the list, the left side is the partial structure predicted to be present, and the right side is the partial structure predicted not
to be present. The partial structure with blue background matches the structural formula selected in the main AI, while thepartial structure with red background does not match. Measured mass spectrum and the predicted composition formula ofeach fragment ion/neutral loss is posted at the top of the screen. It is also possible to confirm and edit comments for eachestimated composition formula.

Measured EI mass spectra
and Predicted composition
formula(Loss compositionformula）

Measured EI MassSpectrum

Predicted EI MassSpectrum

Confirm/edit comments foreach fragment ion andneutral loss

Number of molecular
formulas andstructural
formulas considering
adduct/loss

Structural Formula Prediction by Main AI

Partial Structure Prediction by Support AI• Displays ranked structural formulas in list.
• Selecting a structural formula updates the information that is displayed.
• Below each structural formula is an AI score that indicates the match
percentage between structural formula and mass spectrum.

• Displays the predicted partial structure information• Partial structure predicted and present are on the left, predicted andabsent are on the right.
• Those with a blue background are the partial structures that match theselected structural formulas. Those with a red background are thosethat do not match.

Figure 4 GUI of msFineAnalysis AI

Conclusion
In this MSTips, we introduced our newly-developed software msFineAnalysis AI, which contains AI structural analysis
functionality to enhance qualitative analysis workflow. This software performs automatic structure analysis for unknown
compounds using two AIs (main AI, support AI) that complementarily combine machine learning and deep learning. No
knowledge of mass spectrometry and AI are required as the software automatically interprets complex mass spectra.
Qualitative analysis of GC-MS data can be greatly assisted by using EI and SI data together with msFineAnalysis AI,
especially when trying to identify unknown compounds in complex samples.