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Foods 2019, 8(1), 38; https://doi.org/10.3390/foods8010038

Discrimination of Chinese Liquors Based on Electronic Nose and Fuzzy Discriminant Principal Component Analysis

1,2,*, 1, 3, 1, 1,2 and 1,4
1
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
2
Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry, Jiangsu University, Zhenjiang 212013, China
3
Department of Information Engineering, ChuZhou Vocational Technology College, Chuzhou 239000, China
4
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
Received: 18 December 2018 / Revised: 15 January 2019 / Accepted: 18 January 2019 / Published: 21 January 2019
(This article belongs to the Section Food Engineering and Technology)
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PDF [2233 KB, uploaded 21 January 2019]
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Abstract

The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nose system can quickly detect different types of Chinese liquors according to their aromas. In this study, an e-nose system was designed to identify six types of Chinese liquors, and a novel feature extraction algorithm, called fuzzy discriminant principal component analysis (FDPCA), was developed for feature extraction from e-nose signals by combining discriminant principal component analysis (DPCA) and fuzzy set theory. In addition, principal component analysis (PCA), DPCA, K-nearest neighbor (KNN) classifier, leave-one-out (LOO) strategy and k-fold cross-validation (k = 5, 10, 20, 25) were employed in the e-nose system. The maximum classification accuracy of feature extraction for Chinese liquors was 98.378% using FDPCA, showing this algorithm to be extremely effective. The experimental results indicate that an e-nose system coupled with FDPCA is a feasible method for classifying Chinese liquors. View Full-Text
Keywords: electronic nose; Chinese liquors; fuzzy discriminant principal component analysis; K-nearest neighbor classifier; fuzzy set theory; principal component analysis electronic nose; Chinese liquors; fuzzy discriminant principal component analysis; K-nearest neighbor classifier; fuzzy set theory; principal component analysis
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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 (CC BY 4.0).
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Wu, X.; Zhu, J.; Wu, B.; Zhao, C.; Sun, J.; Dai, C. Discrimination of Chinese Liquors Based on Electronic Nose and Fuzzy Discriminant Principal Component Analysis. Foods 2019, 8, 38.

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