Jeol Ltd.
Jeol Ltd.
02/07/2024 | Press release | Distributed by Public on 02/07/2024 13:40
Update：msFineAnalysis AI Ver.2Unknown CompoundsStructure Analysis Software
Advanced AI technologies enable
structure analysis of unknown compounds
msFineAnalysis AI offers a new structure analysis tool for unknowns that is specifically designed for the JEOL JMST2000GC "AccuTOF™ GC-Alpha." This next generation software adds this structure analysis capability to improve the overall automatic qualitative analysis functionality that was already available with our previous generation msFineAnalysis.
The new "integrated analysis" combines GC/EI high resolution data, GC/soft ionization high resolution data, and "structure analysis" using two AIs (Main AI, Support AI). These advanced AI technologies allow msFineAnalysis AI to provide a unique automatic structure analysis capability that was not previously available for GC-MS qualitative analysis.
#3Deconvolution Detection
#4Two Sample Comparison (Differential Analysis)
#1AI Structure Analysis
Evolving innovative solutions: From molecular formula estimation to structural formula prediction for unknown compounds
For an unknown compound that is not registered in a library database (▼), the conventional msFineAnalysis algorithms automatically suggest a molecular formula. To take it a step further, msFineAnalysis AI enables automatic prediction of structures for all detected components.
The necessity for soft ionization: Reliable acquisition of molecular formula information is the first step in structure analysis!
Mass spectrum of component not registered in library
EI mass spectral data is used for library databases so EI methods are widely used for qualitative analysis of GC-MS samples. However, since EI is a hard ionization method, many fragment ions are observed, and in many cases, it is not uncommon to observe minimal or no signal for the molecular ions.
Additionally, for unknown substances not registered in the library databases, it is difficult to distinguish, using the EI mass spectra alone, whether the largest observed m/z is actually the molecular ion or just a fragment ion. In these cases, a soft ionization method is an effective tool for determining this information.
With the AccuTOF™ GC-Alpha, a variety of soft ionization methods including FI, PI, and CI are optionally available with the system. These techniques can assist in distinguishing ions (e.g. molecular ions and protonated molecules) that provide molecular weight information that then makes it possible to accurately determine the molecular formula information for unknown components.
Since molecular formula information is an important starting point for AI structure analysis, soft ionization is critically important for identifying unknown compounds.
Manual Structure Analysis by Skilled Analyst vs. AI Automatic Structure Analysis
※ Measured with JMS-T2000GC standard configuration PC
The time required for structure analysis was compared for the compounds observed in an acrylic resin measured by Py-GC-TOFMS and were not registered in the NIST library database.
Even an analyst with more than 30 years mass spectrometry experience required approximately 2 hours for structure analysis of 4 components, which is 30 minutes per component. On the other hand, AI structure analysis completed 100 components in less than 7 minutes, which is 4 seconds per component.
AI structure analysis score (similarity) between the structural formula estimated by a skilled analyst and the correct
structural formula, indicating that the structural formula is predicted with good similarity.
Automatic Structure Analysis Using Two AIs:
Stable structure analysis without the need for an online envir onment
msFineAnalysis AI offers an automated structure analysis function.
Based on the structural formula information of more than 100 million organic compounds recognized in the world and calculations using two newly developed AI models, it provides candidate structural formulas even for components that are not registered in the library database.
Evolution of Core Technology "AI Structure Analysis"
msFineAnalysis AI Ver.2 greatly improved structure analisis capability.
The EI mass spectrum prediction model using Graph Convolutional Networks has
been further refined. Both similarity and accuracy have been greatly improved from
ver.1 to provide more accurate results for automated structural analysis.
※AI models are not included with the product. An AI library created with the latest AI models and structural
formula fi ltering functions are included.
The number of registered compounds in the AI library has been expanded to 120
million compounds. Two in-silico libraries are included, expanding the range of
applications in materials analysis and metabolomics.
The AI predicts the retention index (RI) from the structural formula, and the predicted
and measured RI values are used to narrow down the candidate structural formulas.
This function helps to quickly narrow down the correct structural formula.
※This function is only available when the column type is "Standard Non-Polar" or "Semi-Standard Non-Polar.
"Standard Polar" is not available.
AI Structure Analysis Application
Structural analysis of unknown compounds in foods
Flavor components in oysters were analyzed using a combination of HS-SPME GC/MS. AI structure analysis of an unknown component that was previously identified as 1,5-Octadien-3-ol1), yielded 2,560 candidate structural formulas, which were narrowed down to 1,031 candidates using the "OH" substructure filter.
The structural formula proposed by the paper was the 4th hit with an AI score of 875, which is quite high.
1) Kenji Ueda, Koki Yahiro, Yoshihiko Akakabe, J. Oleo Sci. 72, (7) 725-732 (2023)
AI structural analysis result window of the flavor component in oyster
JMS-T2000GC with a HS-SPME (headspace - solid phase microextraction) autosampler
Structural analysis of unknown compounds generated by upcycling of waste polystyrene（MSTips No.456）
A catalytic reaction of waste polystyrene2), commonly used for up-cycling processes, was carried out, and the resulting material was analyzed using AI structure analysis. The characteristic compound detected in the degradation process of waste polystyrene was estimated to be (2-phenylcyclohexyl)benzene. Accurate qualitative analysis for the generated compounds of the catalytic reaction provides useful knowledge for evaluating catalytic reactions and scale-up reactions. AI structure analysis is useful for determining structural formulas for this kind of polymer processing.
2) Zhen Xu, Fuping Pan, Mengqi Sun, Jianjun Xu, Nuwayo Eric Munyaneza, Zacary L. Croft, Gangshu (George) Cai, and Guoliang Liu,. PNAS, 2022, Vol.119,No.34,1-8. https://doi.org/10.1073/pnas.2203346119
AI structure analysis window for characteristic compounds detected in the degradation process
Synthetic scheme for both the degradation process and upcycling process, and sample photographs.
#2Target Analysis
Rapid search for known compounds
The target analysis function automatically searches for compounds based on compositional formula, m/z value, and CAS#.
Preset target analysis lists are available. Additionally, user defined lists can be created and customized for the samples and their analytes of interest. Integrated analysis and AI structural analysis of compounds detected by target analysis are also available.
Target analysis of flavor and off-flavor components in foods
msFineAnalysis AI supports not only non-target analysis but also target analysis. It automatically searches for target compounds based on compositional formula, m/z value and CAS#.
For the data of flavor components in lemon juice, 10 compounds were extracted when analyzed with a target list of 498 off-flavor components. The result window on the lower right shows the detailed analysis result of Citral among the 10 components.
Target list of 498 off-flavor components
Detailed analysis of Citral present in lemon juice
#3Deconvolution Detection
Chromatographic peak deconvolution can detect trace components that may not be obvious in the TICC due to the coelution of several components.
EI: black solid line: TICC, gray peaks: deconvolution peak (blue: currently selected)
FI: green solid line: TICC, gray peaks: deconvolution peak (blue: currently selected)
This step simplifies the data analysis process by defining which ions go with each compound and eliminates the need for creating extracted ion chromatograms (EICs).
Retention index (RI) is a relative index value based on the retention times (RT) for an n-alkane standard mixture.
#4Two Sample Comparison (Differential Analysis)
This function uses the reproducibility of the p-value on the vertical axis and a volcano plot which indicates the intensity ratio between two samples on the horizontal axis.
Detailed analysis - Volcano plot
(A: Reference product, B: Defective product)
This information enables a visual confirmation of the differing components between two samples. For example, it is possible to confirm if a component increases or decreases when comparing a reference product to a defective product or to identify characteristic components in a new material by comparing it to an existing material. For a two sample comparison, it is possible to set n=1, 3, 5 for the number of measurements for each sample.
Major Specifications
JMS-T2000GC AccuTOFTM GC-Alpha
msFineAnalysis AI Ver. 2
msFineAnalysis AI Unknown Compounds Structure Analysis SoftwareAutomatic
GC-TOFMS Application: Qualitative Analysis of Chemical Components in an Herbal Medicine
JMS-T2000GC AccuTOF™ GC-Alpha High Performance Gas Chromatograph - Time-of-Flight Mass SpectrometerAlpha - The New Beginning
The Alpha takes you to a new world of mass spectrometry.
Introducing JMS-T2000GC "AccuTOF™ GC-Alpha", the ultimate GC-MS with superior performance and ease of operation.
Science Basics
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