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Issue 17, 2018
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Electronic nose sensors data feature mining: a synergetic strategy for the classification of beer

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Abstract

The effective feature mining method is one of the key problems in the field of pattern recognition. Moreover, the lack of efficient feature extraction and selection methods has limited the application and development of electronic nose (e-nose) technology. In this study, a synergetic strategy for e-nose sensors data feature mining was proposed in combination with Support Vector Machine (SVM) to determine the beer olfactory information. First, twenty time-domain features and twenty frequency-domain features of e-nose sensors data were extracted to represent the olfactory characteristics of beer. Second, forty features were sorted with variable importance in projection (VIP) scores and forty subsets of multi-features with the best VIP score were generated. Finally, the classification models were established based on SVM, and the best parameter c and g of SVM models was calculated by Genetic Algorithm (GA). Furthermore, the classification performance of each class was evaluated by efficiency value (EFF) in different feature sets. The result indicates that GA-SVM model achieves good classification performance based on the #27 feature set with 81.67% and 96.67% in calibration set and testing set, respectively, and the EFF value is also the highest compared with other feature sets. In conclusion, it indicated that the analytical method can be used as a reliable tool for accurate identification of beer olfactory information.

Graphical abstract: Electronic nose sensors data feature mining: a synergetic strategy for the classification of beer

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Publication details

The article was received on 05 Feb 2018, accepted on 03 Apr 2018 and first published on 03 Apr 2018


Article type: Paper
DOI: 10.1039/C8AY00280K
Citation: Anal. Methods, 2018,10, 2016-2025
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    Electronic nose sensors data feature mining: a synergetic strategy for the classification of beer

    H. Men, Y. Shi, Y. Jiao, F. Gong and J. Liu, Anal. Methods, 2018, 10, 2016
    DOI: 10.1039/C8AY00280K

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