The electronic nose: A critical global review of advances in analytical methods and real-world applications

Abstract

A systematic and comprehensive review of recent literature highlights the significant progress and growing maturity of electronic nose (e-nose) technology. This paper synthesizes advancements from January 2020 to August 2025, demonstrating the e-nose's evolution from a laboratory tool to a versatile, non-destructive, and cost-effective solution for real-world analytical challenges. The review is structured around four key areas: its applications in food and agricultural quality; its expanding use in medical diagnostics; its role in environmental and other monitoring; and the innovative methodologies and technologies driving its performance. Findings show that the e-nose, particularly when combined with data fusion techniques like hyperspectral imaging, often achieves high accuracy in tasks such as product origin tracing and quality control. In medical diagnostics, e-noses are proving effective for non-invasive disease screening, while in environmental science, they offer a means for real-time pollution detection. These practical applications are supported by methodological breakthroughs, including the adoption of advanced machine learning algorithms, such as deep learning and even quantum neural networks, which enhance data interpretation and classification. Despite these advancements, the review identifies critical limitations that future research must address. The need for improved model generalizability, enhanced sensor robustness against environmental factors like humidity, and the development of self-powered and portable systems are key areas for continued investigation. The paper concludes that as these limitations are overcome, the e-nose is poised to become an indispensable tool for rapid, on-site analysis across a wide range of industries, fundamentally transforming how we monitor and analyze chemical signatures.