Electronic nose for the detection and discrimination of volatile organic compounds: Application, challenges, and perspectives

Li, Yanchen1; Wang, Zike1; Zhao, Tianning2; Li, Hua3; Jiang, Jingkun4; Ye, Jianhuai1,5

2024-11

10.1016/j.trac.2024.117958

TrAC - Trends in Analytical Chemistry   影响因子和分区

0165-9936

1879-3142

© 2024 Elsevier B.V.

Electronic nose Volatile organic compound Environmental monitoring Food processing Medical diagnostics

EI ; SCI

英语

第一 ; 通讯

Developing novel sensing materials significantly improves the sensitivity and selectivity of e-nose sensors. Selecting an appropriate pattern recognition algorithm provides a crucial alternative, which can effectively extract valuable information from mixed and noisy signals. The e-nose algorithms include conventional statistical methods such as principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN), and advanced algorithms such as artificial neural network (ANN), convolutional neural network (CNN), and recurrent neural network (RNN). This section summarizes their specific application, as well as their advantages and disadvantages, as outlined in Table 4. This work was funded by National Natural Science Foundation of China (Nos. 42105098 and 42375091) and Shenzhen Science and Technology Innovation Committee (20220814170934001). Supports from Green Tech Fund Environmental Protection Department of HK SAR (GTF202020183), Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area (2021B1212050024), Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks (ZDSYS20220606100604008) are acknowledged.

Chemistry

Chemistry, Analytical

WOS:001315552000001

Elsevier B.V.

20243717029234

EV Compendex

1.Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
2.School of Engineering and Applied Sciences, Harvard University, Cambridge; MA, United States
3.College of Life and Environmental Sciences, Minzu University of China, Beijing, China
4.State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
5.Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen, China

Li, Yanchen,Wang, Zike,Zhao, Tianning,et al. Electronic nose for the detection and discrimination of volatile organic compounds: Application, challenges, and perspectives[J]. TrAC - Trends in Analytical Chemistry,2024,180.

Li, Yanchen,Wang, Zike,Zhao, Tianning,Li, Hua,Jiang, Jingkun,&Ye, Jianhuai.(2024).Electronic nose for the detection and discrimination of volatile organic compounds: Application, challenges, and perspectives.TrAC - Trends in Analytical Chemistry,180.

Li, Yanchen,et al."Electronic nose for the detection and discrimination of volatile organic compounds: Application, challenges, and perspectives".TrAC - Trends in Analytical Chemistry 180(2024).