Stefan Ivanov, Jacek Łukasz Wilk-Jakubowski, Leszek Ciopiński, Łukasz Pawlik, Grzegorz Wilk-Jakubowski, Georgi Mihalev
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.