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College of Electronics and Information Engineering, Southwest University, Chongqing 400715, China





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Academic Editor: Michael Tiemann

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





Author: Jia Yan, Xiuzhen Guo, Shukai Duan * , Pengfei Jia, Lidan Wang, Chao Peng and Songlin Zhang

Source: http://mdpi.com/



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