| INORGANIC MATERIALS AND CERAMIC MATRIX COMPOSITES |
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| Advancements in Electronic Nose Technology and Its Applications |
| MU Shenling1,2, SHEN Wenfeng2,3,*, LYU Dawu2,3, SONG Weijie2,3,*, TAN Ruiqin1,*
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1 Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, Zhejiang, China
2 Ningbo Institute of Material Technology & Engineering, Chinese Academy of Sciences, Ningbo 315201, Zhejiang, China
3 University of Chinese Academy of Sciences, Beijing 100049, China |
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Abstract Electronic nose (E-nose) technology has been widely used as an effective method of olfactory simulation and odor recognition. The E-nose system is composed of an array of gas sensors that utilize their cross-reactivity for qualitative and quantitative gas analysis. Machine learning algorithms are commonly used in the E-nose system, with artificial neural networks being particularly prevalent. In addition to traditional machine learning algorithms, deep learning algorithms are also gradually being applied in E-nose systems. The E-nose system features high selectivity, precision, rapid response, stability, and scalability, and has been applied in various fields, such as toxic gas detection, air quality management, and food freshness and quality prediction. This paper reviews the latest research progress in the gas identification of E-nose systems, including the composition of gas sensor arrays based on MEMS technology, signal acquisition and processing units, and the classification of pattern recognition algorithms. Finally, the current problems and development prospects of E-nose system gas identification are summarized and discussed.
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Published: 25 July 2024
Online: 2024-08-12
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| Fund:Zhejiang Natural Science Foundation (LGF22F010008), Zhejiang Basic Public Welfare Research Project (LGG21F040001), and Ningbo Key Research and Development Plan Project (2023Z021). |
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