Classification of Chinese vinegar varieties using electronic nose and fuzzy Foley-Sammon transformation

Xiao-Hong Wu; Jin Zhu; Bin Wu; Da-Peng Huang; Jun Sun; Chun-Xia Dai

doi:10.1007/s13197-019-04165-y

Classification of Chinese vinegar varieties using electronic nose and fuzzy Foley-Sammon transformation

J Food Sci Technol. 2020 Apr;57(4):1310-1319. doi: 10.1007/s13197-019-04165-y. Epub 2019 Nov 13.

Authors

Xiao-Hong Wu^{1

2}, Jin Zhu¹, Bin Wu³, Da-Peng Huang¹, Jun Sun^{1

2}, Chun-Xia Dai^{1

4}

Affiliations

¹ 1School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China.
² 2Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry, Jiangsu University, Zhenjiang, China.
³ 3Department of Information Engineering, Chuzhou Vocational and Technical College, Chuzhou, China.
⁴ 4School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China.

Abstract

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, K 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.

Keywords: Chinese vinegar; E-nose; Fuzzy Foley–Sammon transformation (FFST); KNN; LDA.