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Sensors 2015, 15(11), 27804-27831; doi:10.3390/s151127804

Electronic Nose Feature Extraction Methods: A Review

College of Electronics and Information Engineering, Southwest University, Chongqing 400715, China
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Author to whom correspondence should be addressed.
Academic Editor: Michael Tiemann
Received: 30 August 2015 / Revised: 10 October 2015 / Accepted: 27 October 2015 / Published: 2 November 2015
(This article belongs to the Special Issue Gas Sensors—Designs and Applications)
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Abstract

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. View Full-Text
Keywords: electronic nose; feature extraction methods; review electronic nose; feature extraction methods; review
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MDPI and ACS Style

Yan, J.; Guo, X.; Duan, S.; Jia, P.; Wang, L.; Peng, C.; Zhang, S. Electronic Nose Feature Extraction Methods: A Review. Sensors 2015, 15, 27804-27831.

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