Two-Dimensional SERS Sensor Array for Identifying and Visualizing the Gas Spatial Distributions of Two Distinct Odor Sources

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  • Lin Chen
    Department of Information Science, Joint Graduate School of Mathematics for Innovation, Kyushu University, Fukuoka 819-0395, Japan
  • Hao Guo
    Department of Electronics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan
  • Cong Wang
    Department of Electronics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan
  • Bin Chen
    Chongqing Key Laboratory of Non-Linear Circuit and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
  • Fumihiro Sassa
    Department of Electronics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan
  • Kenshi Hayashi
    Department of Electronics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan

Description

<jats:p>The spatial distribution of gas emitted from an odor source provides valuable information regarding the composition, size, and localization of the odor source. Surface-enhanced Raman scattering (SERS) gas sensors exhibit ultra-high sensitivity, molecular specificity, rapid response, and large-area detection. In this paper, a SERS gas sensor array was developed for visualizing the spatial distribution of gas evaporated from benzaldehyde and 4-ethylbenzaldehyde odor sources. The SERS spectra of the gas were collected by scanning the sensor array using an automatic detection system. The non-negative matrix factorization algorithm was employed to extract feature and concentration information at each spot on the sensor array. A heatmap image was generated for visualizing the gas spatial distribution using concentration information. Gaussian fitting was applied to process the image for localizing the odor source. The size of the odor source was estimated using the processed image. Moreover, the spectra of benzaldehyde, 4-ethylbenzaldehyde, and their gas mixture were simultaneously detected using one SERS sensor array. The feature information was recognized using a convolutional neural network with an accuracy of 98.21%. As a result, the benzaldehyde and 4-ethylbenzaldehyde odor sources were identified and visualized. Our research findings have various potential applications, including odor source localization, environmental monitoring, and healthcare.</jats:p>

Journal

  • Sensors

    Sensors 24 (3), 790-, 2024-01-25

    MDPI AG

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