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Review

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

by 1, 2 and 1,*
1
Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA 22030, USA
2
Applied Materials, Sunnyvale, CA 94085, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Juan Francisco De Paz Santana, Cristian Pinzón Trejos and Lixin Wang
Sensors 2021, 21(22), 7620; https://doi.org/10.3390/s21227620
Received: 24 October 2021 / Revised: 8 November 2021 / Accepted: 13 November 2021 / Published: 16 November 2021
Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation. View Full-Text
Keywords: electronic nose; gas sensor array; machine learning; neural networks; review electronic nose; gas sensor array; machine learning; neural networks; review
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MDPI and ACS Style

Ye,  .; Liu, Y.; Li, Q. Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods. Sensors 2021, 21, 7620. https://doi.org/10.3390/s21227620

AMA Style

Ye  , Liu Y, Li Q. Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods. Sensors. 2021; 21(22):7620. https://doi.org/10.3390/s21227620

Chicago/Turabian Style

Ye,  Zhenyi, Yuan Liu, and Qiliang Li. 2021. "Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods" Sensors 21, no. 22: 7620. https://doi.org/10.3390/s21227620

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