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  3. Convolutional Neural Networks On Correlation Between Gc-ms Molecular Data And Qcm Gas-sensing Data.
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  3. Convolutional Neural Networks On Correlation Between Gc-ms Molecular Data And Qcm Gas-sensing Data.

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Convolutional Neural Networks on Correlation between GC-MS Molecular Data and QCM Gas-Sensing Data.

Thanisorn Oon-Pitipongsa1, Chaiyanut Jirayupat1,2, Wataru Tanaka1

  • 1Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan.

ACS Sensors
|February 5, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Researchers developed a 1D-CNN model linking quartz crystal microbalance (QCM) sensor data to gas chromatography/mass spectrometry (GC-MS) profiles. This method reconstructs GC-MS maps from QCM signals, enabling direct chemical interpretation.

Keywords:
GC−MS compositionQCM gas sensingconvolutional neural networksprincipal component analysissurface modifications

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Area of Science:

  • Analytical Chemistry
  • Materials Science
  • Machine Learning

Background:

  • Gas chromatography/mass spectrometry (GC-MS) provides detailed chemical composition but is complex.
  • Quartz crystal microbalance (QCM) sensors offer real-time gas detection but lack detailed chemical interpretation.
  • Bridging these techniques is crucial for advanced gas sensing applications.

Purpose of the Study:

  • To establish a correlation between GC-MS compositional data and QCM sensing data.
  • To develop a novel methodology for reconstructing GC-MS profiles from QCM sensor signals.
  • To link QCM sensor responses to chemically interpretable GC-MS patterns.

Main Methods:

  • Development of a one-dimensional convolutional neural network (1D-CNN) model utilizing principal component analysis (PCA).
  • Training the 1D-CNN model to predict PCA scores from GC-MS data using QCM sensor signals.
  • Utilizing nanostructured QCM sensors (ZnO, SnO2, MgO, TiO2) modified via atomic layer deposition.
  • Reconstructing 2D GC-MS maps from QCM time-series data by mapping sensor signals into an invertible PCA latent space.

    • Main Results:

    • The 1D-CNN model achieved high prediction accuracy (average R² = 0.98) for ternary mixtures (ethanol, toluene, dichloromethane).
    • Successfully reconstructed full 2D GC-MS maps directly from QCM time-series data.
    • Demonstrated a method to link QCM sensor responses to specific chemical peak patterns.

    Conclusions:

    • A robust methodology is proposed to correlate and integrate QCM sensing data with GC-MS molecular data.
    • This approach bridges the gap between different gas-sensing data types, enhancing chemical analysis.
    • The findings pave the way for advanced electronic nose systems with improved chemical identification capabilities.