Tan Yushuo, Chen Yating, Zhao Yundi, Liu Minggao, Wang Zhiyao, Du Liping, Wu Chunsheng, Xu Xiaozhao
Institute of Medical Engineering, Department of Biophysics, School of Basic Medical Sciences, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, China; Modern Postal College, ShiJiaZhuang Posts and Telecommunications Technical College, Shijiazhuang, 050021, China.
Institute of Medical Engineering, Department of Biophysics, School of Basic Medical Sciences, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, China.
Talanta. 2025 Feb 1;283:127140. doi: 10.1016/j.talanta.2024.127140. Epub 2024 Oct 31.
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.
电子鼻技术已成为各个领域的关键工具，广泛应用于气味识别、浓度评估和预测任务。本文综述了电子鼻系统开发及其算法应用的最新进展，重点介绍了各种方法和深度学习技术在气味分类和浓度预测中的作用。此外，我们深入探讨了模型评估方法，包括多维性能评估和交叉验证。未来趋势包括更广泛的应用领域、先进的漂移校正技术、全面的多因素分析以及增强处理未知干扰物的能力。这些趋势将推动电子鼻技术在科研和实际应用中取得重大突破，巩固电子鼻系统在环境监测、生物医学和公共安全等许多领域的关键工具地位。
