Open AccessClassification of Chinese vinegar varieties using electronic nose and fuzzy Foley–Sammon transformation
Author(s) -
Xiaohong Wu,
Jin Zhu,
Bin Wu,
Dapeng Huang,
Jun Sun,
Chunxia Dai
Publication year - 2019
Publication title -
journal of food science and technology/journal of food science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.656
H-Index - 68
eISSN - 0975-8402
pISSN - 0022-1155
DOI - 10.1007/s13197-019-04165-y
Subject(s) - electronic nose , artificial intelligence , pattern recognition (psychology) , principal component analysis , linear discriminant analysis , feature extraction , mathematics , computer science , classifier (uml) , fuzzy logic
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.