Wu Xiaohong, Zhu Jin, Wu Bin, Zhao Chao, Sun Jun, Dai Chunxia
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry, Jiangsu University, Zhenjiang 212013, China.
Foods. 2019 Jan 21;8(1):38. doi: 10.3390/foods8010038.
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 ( = 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.
白酒质量检测是白酒行业中的一个重要环节，中国白酒的品质部分取决于其香气。电子鼻是一种人工嗅觉技术。电子鼻系统能够根据白酒的香气快速检测出不同类型的中国白酒。在本研究中，设计了一种电子鼻系统来识别六种中国白酒，并通过结合判别主成分分析（DPCA）和模糊集理论，开发了一种名为模糊判别主成分分析（FDPCA）的新型特征提取算法，用于从电子鼻信号中提取特征。此外，在电子鼻系统中采用了主成分分析（PCA）、DPCA、K近邻（KNN）分类器、留一法（LOO）策略和k折交叉验证（k = 5、10、20、25）。使用FDPCA对中国白酒进行特征提取的最大分类准确率为98.378%，表明该算法极其有效。实验结果表明，结合FDPCA的电子鼻系统是一种对中国白酒进行分类的可行方法。
