Version 1
: Received: 6 May 2017 / Approved: 8 May 2017 / Online: 8 May 2017 (08:57:56 CEST)
How to cite:
Men, H.; Shi, Y.; Fu, S.; Jiao, Y.; Qiao, Y.; Liu, J. Discrimination of Beer Based on E-tongue and E-nose Combined with SVM: Comparison of Different Variable Selection Methods by PCA, GA-PLS and VIP. Preprints2017, 2017050054. https://doi.org/10.20944/preprints201705.0054.v1
Men, H.; Shi, Y.; Fu, S.; Jiao, Y.; Qiao, Y.; Liu, J. Discrimination of Beer Based on E-tongue and E-nose Combined with SVM: Comparison of Different Variable Selection Methods by PCA, GA-PLS and VIP. Preprints 2017, 2017050054. https://doi.org/10.20944/preprints201705.0054.v1
Men, H.; Shi, Y.; Fu, S.; Jiao, Y.; Qiao, Y.; Liu, J. Discrimination of Beer Based on E-tongue and E-nose Combined with SVM: Comparison of Different Variable Selection Methods by PCA, GA-PLS and VIP. Preprints2017, 2017050054. https://doi.org/10.20944/preprints201705.0054.v1
APA Style
Men, H., Shi, Y., Fu, S., Jiao, Y., Qiao, Y., & Liu, J. (2017). Discrimination of Beer Based on E-tongue and E-nose Combined with SVM: Comparison of Different Variable Selection Methods by PCA, GA-PLS and VIP. Preprints. https://doi.org/10.20944/preprints201705.0054.v1
Chicago/Turabian Style
Men, H., Yu Qiao and Jingjing Liu. 2017 "Discrimination of Beer Based on E-tongue and E-nose Combined with SVM: Comparison of Different Variable Selection Methods by PCA, GA-PLS and VIP" Preprints. https://doi.org/10.20944/preprints201705.0054.v1
Abstract
Multi-sensor data fusion of E-tongue and E-nose can provide a more comprehensive and more accurate analysis results. However, it also brings some redundant information, it is a hot issue to reduce the feature dimension for pattern recognition. In this paper, the taste-olfactory data fusion based on E-tongue and E-nose combined with Support Vector Machine (SVM) was used to classify five different beers. First, the taste and olfactory feature information were obtained based on E-tongue and E-nose. Second, the original feature data of single system were fused, then Principal Component Analysis (PCA) was applied to extract principal components, Genetic Algorithm-Partial Least Squares (GA-PLS) was used to select the characteristic variables, 20 subsets were generated with those variables based on the best Variable Importance of Projection (VIP) score. Finally, the classification models based on SVM were established, also c and g of SVM were calculated by Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), the classification results of all subsets were obtained. The results showed that the classification accuracy using data fusion was much higher over single E-tongue and single E-nose, and the variable selection method by VIP had the best classification performance in #12 subset coupled with GA-SVM.
Keywords
E-tongue; E-nose; data fusion; variable selection; patter recognition; beer
Subject
Chemistry and Materials Science, Electrochemistry
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
