msFineAnalysis IQ

GC-QMS qualitative analysis software

A Next‑Generation GC–QMS Qualitative Analysis Solution
Powered by Integrated Analysis and AI Structure Analysis!

msFineAnalysis Auto-qualitative Analysis Software

The msFineAnalysis IQ is an automatic qualitative analysis software that enables "integrated analysis" by combining the library database (DB) search using EI data and molecular weight confirmation using soft ionization data. msFineAnalysis iQ improves analysis accuracy, shortens work hours, and improves work efficiency.

msFineAnalysis IQ

For the single quad and triple quad mass specs

msFineAnalysis AI

For the AccuTOF GC-Alpha

Features

Soft Ionization and AI Transform GC‑MS Qualitative Analysis

From molecular ion acquisition to structure estimation, the entire workflow is fully automated.

Soft Ionization and AI Transform GC‑MS Qualitative Analysis

Feature #1: Integrated Analysis

Break away from qualitative analysis based only on NIST database searches!

More reliable qualitative analysis is possible by “Integrated analysis”

We have consolidated the verification of soft ionization data, which previously required manual work and multiple software tools, into a single software platform and an integrated analysis workflow. By automating the analysis process, the time required for qualitative analysis using soft ionization data is significantly reduced.
Work Screen of Integrated Analysis Results
Work Screen of Integrated Analysis Results
Analysis Screen of Individual Compounds
By combining retention index information with isotopic pattern analysis, the software provides qualitative analysis results with even higher confidence.
Analysis Screen of Individual Compounds

msFineAnalysis IQ Analysis Workflow

Integrated analysis provides more accurate results than qualitative analysis based solely on NIST database searches. Even when NIST database searches do not yield strong candidates, AI structure analysis enables reliable structural estimation.
msFineAnalysis IQ Analysis Workflow

Necessity of Soft-Ionization Data

The risk of qualitative analysis by EI library search alone…are you sure that the identification is correct?
 In EI, the ionization energy is high, and molecular ions are sometimes not observed. In the EI spectrum of component A (right figure), the molecular ion is also not detected. Although multiple candidate compounds are obtained from the NIST database search, relying solely on EI data may lead to selecting the No. 1 candidate simply because it has the highest match factor.
In contrast, when component A is analyzed using a soft‑ionization (SI) method, a molecular ion at m/z 314 is clearly observed, indicating that the No. 2 candidate — with a molecular weight of 314 — is more plausible.
Qualitative analysis based only on EI data carries a risk of misidentification, whereas combining database searching with SI data analysis provides results with much higher confidence. msFineAnalysis IQ automatically performs this type of qualitative analysis —“Integrated Analysis”— which combines EI and SI data.

Feature #2: AI Structure Analysis

Qualitative analysis is possible even for compounds not registered in the NIST database!

AI structure analysis rapidly proposes structural candidates for unknown compounds.

We developed two types of AI models capable of predicting EI mass spectra and retention index values from chemical structures. Using these models, we constructed a database (AI Library) containing approximately 200 million compounds, enabling qualitative analysis of compounds not registered in the NIST database with an operation style similar to conventional NIST searches.
In msFineAnalysis IQ, AI structure analysis rapidly provides reliable structural candidates by narrowing down the possibilities using molecular‑weight information obtained from soft ionization data.
Previous msFineAnalysis IQ
Soft ionization data provided qualitative information that complemented NIST database searches.
Previous msFineAnalysis iQ
Latest msFineAnalysis IQ
AI structure analysis makes it possible to estimate structures even for compounds not registered in the NIST database.
Latest msFineAnalysis IQ

High-Accuracy AI Model

The AI model used to predict mass spectra incorporates the technology developed through the creation of msFineAnalysis AI, a software for JEOL GC‑HRTOFMS, the JMS-T2000GC AccuTOFTM GC-Alpha 2.0.
The histogram below shows the cosine similarity between experimental and predicted mass spectra for 10,000 compounds used for evaluation.
In the previous AI model, the average cosine similarity was 0.72, whereas the latest model achieves an improved average of 0.86.
High-Accuracy AI Model

High-Accuracy AI Model for Structure Prediction

The table below shows the evaluation results for 10,000 compounds, assessing whether AI structure analysis presents the correct structure among the top candidates.
These results confirm that the method provides excellent structural prediction performance.
High-Accuracy AI Model for Structure Prediction

Qualitative Analysis of Pyrolysis Products of Acrylic Resin Using Py‑GC‑QMS

Obtaining reference standards for polymer pyrolysis products is often difficult, and dimers and trimers in particular are frequently not registered in the NIST database. The example below shows the analysis of a dimer derived from an acrylic resin (a methyl methacrylate/methyl acrylate copolymer), which is not included in the NIST database.
We examined how highly the structure described in the literature* appears in the candidate list generated by AI structure analysis in msFineAnalysis IQ, and in this case, the correct structure was presented as the second‑ranked candidate.
Qualitative Analysis of Pyrolysis Products of Acrylic Resin Using Py‑GC‑QMS

Feature #3: Target Analysis

Quickly search for target compounds such as odor compounds and additives!

Target analysis automatically searches for compounds based on compositional formula, m/z value, and CAS#. Target lists can be freely created and edited, and several types of pre-made target lists are also installed. Integrated analysis of compounds detected by target analysis is available.
Editing the target compound list
Editing the target compound list
Work Screen of Target Analysis Result
Work Screen of Target Analysis Result

Analysis of Aroma Compounds in Hamburgers Using Microchamber/TD-GC-QMS (MSTips No. 486)

Commercially available hamburgers were measured using microchamber/TD-GC-MS, and target analysis was performed using a selfmade list of food flavor compounds. As a result, 4 out of 7 compounds registered in the list met the criteria (background color: blue). It was possible to rapidly search for characteristic aroma compounds derived from hamburger spices and herbs.
Analysis of Aroma Compounds in Hamburgers Using Microchamber/TD-GC-QMS
application details: MSTips No. 486

Feature #4: Deconvolution Detection

Co-eluting compounds that appear as a single peak on the TICC and trace compounds hidden by chemical noise are also detected!

Deconvolution Detection

Feature #5: netCDF (ANDI-MS) Data Analysis

Analysis of netCDF (ANDI‑MS) data, the common data format for GC‑MS, is supported!

Data acquired on legacy JEOL GC‑QMS series instruments can also be analyzed, provided that the data are converted into the netCDF format.
netCDF (ANDI-MS) Data Analysis

Feature #6: Differential Analysis

Quickly extract the differences between two samples, such as good or defective products, differences in origin, differences in manufacturing methods, etc!

Work Screen for Difference Analysis Results
Work Screen for Difference Analysis Results
msFineAnalysis IQ performs difference analysis using statistical hypothesis testing. The statistical hypothesis test uses GC/EI data (two samples measured in replicates, e.g., n=3 or n=5) to determine if there is “repeatability” and “difference” in the detected compounds between the samples. After classification of compounds that are characteristic of each sample and those that are common to all samples, integrated analysis using GC/EI and SI data is performed automatically.
Volcano plot
In the volcano plot, each circle corresponds to a single compound, and the size of the circle reflects the peak area. The horizontal and vertical axes are shown below:
Volcano plot
【Horizontal axis】
Logarithm of the intensity ratio between two samples (Fold-change)
The larger the absolute value, the greater the intensity ratio between samples. 
【Vertical axis】
Negative value of logarithm of p-value the larger the value, the higher the reproducibility.
A volcano plot enables intuitive visualization of both differential components between samples and their reproducibility.

Difference analysis of chocolates with different cacao content using HS-SPME-GC-QMS (MSTips No. 438)

In each sample, compounds found in chocolate aroma, such as aldehydes, esters, carboxylic acids and nitrogen-containing pyrazines, were detected.
Compound ID:032 with the highest intensity at approximately 95% cacao content was estimated to be pyrazine, tetramethyl-. Pyrazine, tetramethyl- is an aroma produced by roasting cacao, and it is suggested that it is strongly detected in this sample with a higher cacao content. The compound eluted at a time close to that of a common component, acetic acid, between the two samples. However, it could be detected by deconvolution peak detection.
Difference analysis of chocolates with different cacao content using HS-SPME-GC-QMS
Difference analysis of chocolates with different cacao content using HS-SPME-GC-QMS
application details: MSTips No. 438

Difference analysis of water-based inks of different colors (cyan and magenta) (MSTips No. 396)

Compounds found specifically in the cyan and magenta are in the left and right regions, respectively, of the volcano plot. Although quantitatively smaller than common components such as water and isopropyl alcohol in the center, we found that ethanol, 2,2'-oxybis- (diethylene glycol) and four other compounds were specifically present in cyan, as opposed to caprolactam, which is specifically present in magenta.
Difference analysis of water-based inks of different colors (cyan and magenta)
application details: MSTips No. 396

Applicable Models

    msFineAnalysis IQ

    Notes

    msFine Analysis IQ

    • The MS-06024N23 (NIST23 database) is required.
    • This product operates on the PC with the Windows® 11 or later.
    • The display resolution must be 1920 x 1080 or higher.
    • AI structure analysis is available with the AI library which is an optional product.
    Notice:
    Windows® is either a registered trademark or a trademark of Microsoft Corporation in the United States and/or other countries.

    Application Notes

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