电子鼻
标准化
风险分析(工程)
系统工程
钥匙(锁)
数据科学
计算机科学
工程类
可扩展性
补偿(心理学)
医疗保健
环境科学
化学传感器
模式
跟踪系统
质量(理念)
大数据
作者
Stefan Ivanov,Jacek Łukasz Wilk-Jakubowski,Leszek Ciopiński,Łukasz Pawlik,Grzegorz Wilk-Jakubowski,Georgi Mihalev
出处
期刊:Applied sciences
[Multidisciplinary Digital Publishing Institute]
日期:2025-10-07
卷期号:15 (19): 10776-10776
被引量:2
摘要
Electronic nose (e-nose) systems have emerged as transformative tools for odor and gas analysis, leveraging advances in nanomaterials, sensor arrays, and machine learning (ML) to mimic biological olfaction. This review synthesizes recent developments in e-nose technology, focusing on innovations in sensor design (e.g., graphene-based nanomaterials, MEMS, and optical sensors), drift compensation techniques, and AI-driven data processing. We highlight key applications across healthcare (e.g., non-invasive disease diagnostics via breath analysis), food quality monitoring (e.g., spoilage detection and authenticity verification), and environmental management (e.g., pollution tracking and wastewater treatment). Despite progress, challenges such as sensor selectivity, long-term stability, and standardization persist. The paper underscores the potential of e-noses to replace conventional analytical methods, offering portability, real-time operation, and cost-effectiveness. Future directions include scalable fabrication, robust ML models, and IoT integration to expand their practical adoption.
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