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WMDCNN: A deep learning method for gas sensor arrays utilizing feature correlation and CACLoss.

Hui Tian1, Wenhao Sui1, Huilin Lu1

  • 1School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, Henan, 450001, China.

Talanta
|June 27, 2026
PubMed
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This study introduces a novel deep learning framework for gas sensor arrays, enhancing accuracy in mixed gas recognition. The wise multi-dimensional convolutional neural network (WMDCNN) offers an efficient and drift-tolerant solution.

Area of Science:

  • Chemical Sensing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional machine learning for gas recognition is resource-efficient but limited by cross-interference.
  • Deep learning excels at complexity but demands high computational resources.
  • Existing methods struggle with accurate identification of complex gas mixtures.

Purpose of the Study:

  • To develop a novel deep learning framework for enhanced gas sensor array recognition.
  • To improve accuracy and computational efficiency in mixed gas classification and concentration prediction.
  • To address limitations of existing methods in handling complex gas mixtures and sensor drift.

Main Methods:

  • Feature correlation analysis to construct discriminative heuristic feature subsets using Pearson correlation coefficients.
Keywords:
Accurate classificationFeature correlationFeature extractionGas recognitionSensor array

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  • Development of a wise multi-dimensional convolutional neural network (WMDCNN) for processing feature vectors.
  • Integration of class anchor clustering loss (CACLoss) to regularize latent space for improved class separation.

    • Main Results:

    • Achieved superior mixed-gas classification accuracy up to 97.51% and 97.89% on proprietary and public datasets.
    • Reduced root mean square error (RMSE) by 37.85% to 59.66% in concentration prediction tasks compared to baseline models.
    • Demonstrated a computationally efficient and drift-tolerant solution for gas recognition.

    Conclusions:

    • The proposed WMDCNN framework provides a highly accurate and efficient solution for mixed gas recognition.
    • The novel approach effectively handles complex gas mixtures and sensor drift.
    • This framework advances gas sensing technology through optimized feature selection and deep learning architecture.