Wu Xiao-Hong, Zhu Jin, Wu Bin, Huang Da-Peng, Sun Jun, Dai Chun-Xia
1School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China.
2Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry, Jiangsu University, Zhenjiang, China.
J Food Sci Technol. 2020 Apr;57(4):1310-1319. doi: 10.1007/s13197-019-04165-y. Epub 2019 Nov 13.
Due to the difference of raw materials and brewing technology, the quality and flavours of vinegar are different. Different kinds of vinegar have different functions and effects. Therefore, it is important to classify the vinegar varieties correctly. This work presented a new fuzzy feature extraction algorithm, called fuzzy Foley-Sammon transformation (FFST), and designed the electronic nose (E-nose) system for classifying vinegar varieties successfully. Principal component analysis (PCA) and standard normal variate (SNV) were used as the data preprocessing algorithms for the E-nose system. FFST, Foley-Sammon transformation (FST) and linear discriminant analysis (LDA) were used to extract discriminant information from E-nose data, respectively. Then, nearest neighbor (KNN) served as a classifier for the classification of vinegar varieties. The highest identification accuracy rate was 96.92% by using the FFST and KNN. Therefore, the E-nose system combined with the FFST was an effective method to identify Chinese vinegar varieties and this method has wide application prospects.
由于原材料和酿造工艺的差异，醋的品质和风味各不相同。不同种类的醋具有不同的功能和功效。因此，正确对醋的品种进行分类很重要。这项工作提出了一种新的模糊特征提取算法，称为模糊Foley-Sammon变换（FFST），并成功设计了用于醋品种分类的电子鼻（E-nose）系统。主成分分析（PCA）和标准正态变量变换（SNV）被用作E-nose系统的数据预处理算法。FFST、Foley-Sammon变换（FST）和线性判别分析（LDA）分别用于从E-nose数据中提取判别信息。然后，最近邻（KNN）用作醋品种分类的分类器。使用FFST和KNN时，最高识别准确率为96.92%。因此，结合FFST的E-nose系统是识别中国醋品种的有效方法，该方法具有广阔的应用前景。
