Recent advances in signal processing algorithms for electronic noses - Tan, Yushuo; Chen, Yating; Zhao, Yundi; Liu, Minggao; Wang, Zhiyao; Du, Liping; Wu, Chunsheng; Xu, Xiaozhao (Talanta (Oxford), 2025)
Electronic nose (e-nose) technology has emerged as a pivotal tool in various domains, which has been widely utilized for odor identification, concentration evaluation, and prediction tasks. This review provides a comprehensive survey on the most recent advances in the development of e-nose systems and their algorithmic applications, emphasizing the roles of various methodologies and deep learning technologies in odor classification and concentration forecasting. Additionally, we delve into model evaluation methods, including multidimensional performance assessment and cross-validation. Future trends encompass broader application domains, advanced drift correction techniques, comprehensive multifactorial analysis, and enhanced capabilities for dealing with unknown interferents. These trends are set to propel significant breakthroughs in e-nose technology within scientific research and practical applications, solidifying the e-nose system as a crucial tool in many areas such as environmental monitoring, biomedicine, and public safety. [Display omitted] •The most recent advances in e-noses and their algorithmic applications are outlined.•Various methodologies and deep learning technologies in odor classification and concentration forecasting are summarized.•Future trends of e-noses as crucial tools in many areas are proposed and discussed.