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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MDPI AG
2021
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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) |
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electronic nose gas sensor array machine learning neural networks review Chemical technology TP1-1185 |
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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 |
_version_ |
1718410461819437056 |