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Electronic Nose Feature Extraction Methods: A Review

Many research groups in academia and industry are focusing on the performance improvement of electronic nose (E-nose) systems mainly involving three optimizations, which are sensitive material selection and sensor array optimization, enhanced feature extraction methods and pattern recognition method...

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Detalles Bibliográficos
Autores principales: Yan, Jia, Guo, Xiuzhen, Duan, Shukai, Jia, Pengfei, Wang, Lidan, Peng, Chao, Zhang, Songlin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701255/
https://www.ncbi.nlm.nih.gov/pubmed/26540056
http://dx.doi.org/10.3390/s151127804
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author Yan, Jia
Guo, Xiuzhen
Duan, Shukai
Jia, Pengfei
Wang, Lidan
Peng, Chao
Zhang, Songlin
author_facet Yan, Jia
Guo, Xiuzhen
Duan, Shukai
Jia, Pengfei
Wang, Lidan
Peng, Chao
Zhang, Songlin
author_sort Yan, Jia
collection PubMed
description Many research groups in academia and industry are focusing on the performance improvement of electronic nose (E-nose) systems mainly involving three optimizations, which are sensitive material selection and sensor array optimization, enhanced feature extraction methods and pattern recognition method selection. For a specific application, the feature extraction method is a basic part of these three optimizations and a key point in E-nose system performance improvement. The aim of a feature extraction method is to extract robust information from the sensor response with less redundancy to ensure the effectiveness of the subsequent pattern recognition algorithm. Many kinds of feature extraction methods have been used in E-nose applications, such as extraction from the original response curves, curve fitting parameters, transform domains, phase space (PS) and dynamic moments (DM), parallel factor analysis (PARAFAC), energy vector (EV), power density spectrum (PSD), window time slicing (WTS) and moving window time slicing (MWTS), moving window function capture (MWFC), etc. The object of this review is to provide a summary of the various feature extraction methods used in E-noses in recent years, as well as to give some suggestions and new inspiration to propose more effective feature extraction methods for the development of E-nose technology.
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spelling pubmed-47012552016-01-19 Electronic Nose Feature Extraction Methods: A Review Yan, Jia Guo, Xiuzhen Duan, Shukai Jia, Pengfei Wang, Lidan Peng, Chao Zhang, Songlin Sensors (Basel) Review Many research groups in academia and industry are focusing on the performance improvement of electronic nose (E-nose) systems mainly involving three optimizations, which are sensitive material selection and sensor array optimization, enhanced feature extraction methods and pattern recognition method selection. For a specific application, the feature extraction method is a basic part of these three optimizations and a key point in E-nose system performance improvement. The aim of a feature extraction method is to extract robust information from the sensor response with less redundancy to ensure the effectiveness of the subsequent pattern recognition algorithm. Many kinds of feature extraction methods have been used in E-nose applications, such as extraction from the original response curves, curve fitting parameters, transform domains, phase space (PS) and dynamic moments (DM), parallel factor analysis (PARAFAC), energy vector (EV), power density spectrum (PSD), window time slicing (WTS) and moving window time slicing (MWTS), moving window function capture (MWFC), etc. The object of this review is to provide a summary of the various feature extraction methods used in E-noses in recent years, as well as to give some suggestions and new inspiration to propose more effective feature extraction methods for the development of E-nose technology. MDPI 2015-11-02 /pmc/articles/PMC4701255/ /pubmed/26540056 http://dx.doi.org/10.3390/s151127804 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Yan, Jia
Guo, Xiuzhen
Duan, Shukai
Jia, Pengfei
Wang, Lidan
Peng, Chao
Zhang, Songlin
Electronic Nose Feature Extraction Methods: A Review
title Electronic Nose Feature Extraction Methods: A Review
title_full Electronic Nose Feature Extraction Methods: A Review
title_fullStr Electronic Nose Feature Extraction Methods: A Review
title_full_unstemmed Electronic Nose Feature Extraction Methods: A Review
title_short Electronic Nose Feature Extraction Methods: A Review
title_sort electronic nose feature extraction methods: a review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701255/
https://www.ncbi.nlm.nih.gov/pubmed/26540056
http://dx.doi.org/10.3390/s151127804
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