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

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,...

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Autores principales:  Zhenyi Ye, Yuan Liu, Qiliang Li
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
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Acceso en línea:https://doaj.org/article/521eaab819374fd09d65dc2e63ca3a9a
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spelling oai:doaj.org-article:521eaab819374fd09d65dc2e63ca3a9a2021-11-25T18:57:58ZRecent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods10.3390/s212276201424-8220https://doaj.org/article/521eaab819374fd09d65dc2e63ca3a9a2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7620https://doaj.org/toc/1424-8220Machine 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. Zhenyi YeYuan LiuQiliang LiMDPI AGarticleelectronic nosegas sensor arraymachine learningneural networksreviewChemical technologyTP1-1185ENSensors, Vol 21, Iss 7620, p 7620 (2021)
institution DOAJ
collection DOAJ
language EN
topic electronic nose
gas sensor array
machine learning
neural networks
review
Chemical technology
TP1-1185
spellingShingle electronic nose
gas sensor array
machine learning
neural networks
review
Chemical technology
TP1-1185
 Zhenyi Ye
Yuan Liu
Qiliang Li
Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods
description 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.
format article
author  Zhenyi Ye
Yuan Liu
Qiliang Li
author_facet  Zhenyi Ye
Yuan Liu
Qiliang Li
author_sort  Zhenyi Ye
title Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods
title_short Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods
title_full Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods
title_fullStr Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods
title_full_unstemmed Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods
title_sort recent progress in smart electronic nose technologies enabled with machine learning methods
publisher MDPI AG
publishDate 2021
url https://doaj.org/article/521eaab819374fd09d65dc2e63ca3a9a
work_keys_str_mv AT zhenyiye recentprogressinsmartelectronicnosetechnologiesenabledwithmachinelearningmethods
AT yuanliu recentprogressinsmartelectronicnosetechnologiesenabledwithmachinelearningmethods
AT qiliangli recentprogressinsmartelectronicnosetechnologiesenabledwithmachinelearningmethods
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