Review on Algorithm Design in Electronic Noses: Challenges, Status, and Trends

Abstract

Electronic noses, or e-noses, refer to systems powered by chemical gas sensors, signal processing, and machine learning algorithms for realizing artificial olfaction. They play a crucial role in various applications for decoding chemical environmental information. Despite decades of advances in gas-sensing technology and artificial intelligence, the reliability and stability of e-nose systems remain challenging, which is also one of the major obstacles that prevent e-noses from large-scale deployment. This paper presents a wide-ranging and structured review of the methods and algorithms developed in the e-nose literature over the past few decades. The review adopts a problem-oriented taxonomy aimed at clarifying the motivations and challenges of different methods and algorithms and their pros and cons. Moreover, several promising research directions in this field have been presented.