文献类型：期刊文献

英文题名：Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array

作者：Mei, Haixia[1];Peng, Jingyi[1];Wang, Tao[2];Zhou, Tingting[4];Zhao, Hongran[4];Zhang, Tong[4];Yang, Zhi[3]

机构：[1]Changchun Univ, Key Lab Intelligent Rehabil & Barrier Free Disable, Minist Educ, Changchun 130022, Peoples R China;[2]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China;[3]Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Dept Micro Nano Elect, Natl Key Lab Adv Micro & Nano Manufacture Technol, Shanghai 200240, Peoples R China;[4]Jilin Univ, Coll Elect Sci & Engn, State Key Lab Integrated Optoelect, Changchun 130012, Peoples R China

年份：2024

卷号：16

期号：1

外文期刊名：NANO-MICRO LETTERS

收录：;EI(收录号：20243416898785);WOS:【SCI-EXPANDED(收录号:WOS:001291739800001)】；

基金：This work was supported by the National Key Research and Development Program of China (2021YFB3200400), the National Natural Science Foundation of China (62371299, 62301314, and 62020106006), the China Postdoctoral Science Foundation (2023M732198). We also acknowledge analysis support from the Instrumental Analysis Center of Shanghai Jiao Tong University and the Center for Advanced Electronic Materials and Devices of Shanghai Jiao Tong University. The computations in this paper were run on the pi 2.0 cluster supported by the Center for High Performance Computing at Shanghai Jiao Tong University.

语种：英文

外文关键词：Pattern recognition; Sensor array; Chemiresistive gas sensor; Cross-sensitivity; Artificial olfactory

摘要：The types, working principles, advantages and limitations of pattern recognition methods based on chemiresistive gas sensor array are reviewed and discussed comprehensively.Outstanding and novel advancements in the application of machine learning methods for gas recognition in different important areas are compared, summarized and evaluated.The current challenges and future prospects of machine learning methods in artificial olfactory systems are discussed and justified. As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.