Gas Recognition in E-Nose System: A Review

• Published in: IEEE transactions on biomedical circuits and systems Vol. 16; no. 2; pp. 169 - 184
• Main Authors: Chen, Hong, Huo, Dexuan, Zhang, Jilin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; artificial neural network; Artificial neural networks; Classification algorithms; Electronic Nose; Electronic noses; Energy efficiency; energy-efficient hardware; Estimation; Firing pattern; Gas recognition; Gas sampling; Gas sensors; Gases; Hardware; Neural networks; Neural Networks, Computer; Parameters; Principal Component Analysis; Principal components analysis; Recognition; Sensor arrays; spiking neural network; Support vector machines; Task analysis
• ISSN: 1932-4545; 1940-9990; 1940-9990
• DOI: 10.1109/TBCAS.2022.3166530

Gas recognition is essential in an electronic nose (E-nose) system, which is responsible for recognizing multivariate responses obtained by gas sensors in various applications. Over the past decades, classical gas recognition approaches such as principal component analysis (PCA) have been widely applied in E-nose systems. In recent years, artificial neural network (ANN) has revolutionized the field of E-nose, especially spiking neural network (SNN). In this paper, we investigate recent gas recognition methods for E-nose, and compare and analyze them in terms of algorithms and hardware implementations. We find each classical gas recognition method has a relatively fixed framework and a few parameters, which makes it easy to be designed and perform well with limited gas samples, but weak in multi-gas recognition under noise. While ANN-based methods obtain better recognition accuracy with flexible architectures and lots of parameters. However, some ANNs are too complex to be implemented in portable E-nose systems, such as deep convolutional neural networks (CNNs). In contrast, SNN-based gas recognition methods achieve satisfying accuracy and recognize more types of gases, and could be implemented with energy-efficient hardware, which makes them a promising candidate in multi-gas identification.