Near-Infrared Spectral MEMS Gas Sensor for Multi-Component Food Gas Detection

作     者：Yan, Xiaojian Tan, Yao Wang, Yi Chen, Gongdai Xia, Weigao Zhou, Gang Luo, Hongliang Liu, Hao Gong, Tianxun Zhang, Xiaosheng 

作者机构：Univ Elect Sci & Technol China Sch Integrated Circuit Sci & Engn Chengdu 611731 Peoples R China Panovas Technol Co Ltd Chengdu 610041 Peoples R China 

出 版 物：《MICROMACHINES》 (Micromachines)

年 卷 期：2025年第16卷第2期

页      面：135-135页

核心收录：

学科分类：08[工学] 0804[工学-仪器科学与技术] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0802[工学-机械工程] 0702[理学-物理学] 

基　　金：National Key Research and Development Program of China Key R&D Program of Mianyang [2023ZYDF019] National Natural Science Foundation of China Key R&D Program of Sichuan Province [2023YFQ0110, 2022JDTD0020, 2020ZHCG0038] Natural Science Foundation of Sichuan [2022NSFSC1941] 2022YFB3206100 

主　　题：MEMS micro-electromechanical systems near-infrared spectroscopy gas sensor 

摘      要：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.