Abstract
The complex application environments of gas detection, such as in industrial process monitoring and control, atmospheric and environmental monitoring, and food safety, require real-time and online high-sensitivity gas detection, as well as the accurate identification and quantitative analysis of gas samples. Despite the progress in gas analysis and detection methods, high-precision and high-sensitivity detection requirements for target gases of multiple components in mixed gases are still challenging. Here, we demonstrate a micro-electromechanical system (MEMS) with near-infrared (NIR) spectral gas detection technology and spectral model training, which is used to improve the detection and classification of multi-component gases in food. During blind sample testing, the NIR spectral gas sensor demonstrated over 90% accuracy in identifying mixed gases, as well as achieving the classification of ethanol concentration. We envision that our design strategy of an NIR spectral gas sensor could enhance the gas detection and distinguishing ability under the conditions of background gas interference and cross-interference in multi-component detection.
Published Version
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