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.  FFST, Foley-Sammon transformation (FST) and linear discriminant analysis (LDA) were used to extract discriminant information from E-nose data, respectively.  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.
FFST
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.  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.
E-nose
Principal component analysis (PCA) and standard normal variate (SNV) were used as the data preprocessing algorithms for the E-nose system. 
PCA
Principal component analysis (PCA) and standard normal variate (SNV) were used as the data preprocessing algorithms for the E-nose system. 
SNV
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.  FFST, Foley-Sammon transformation (FST) and linear discriminant analysis (LDA) were used to extract discriminant information from E-nose data, respectively.  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.
FST
FFST, Foley-Sammon transformation (FST) and linear discriminant analysis (LDA) were used to extract discriminant information from E-nose data, respectively. 
LDA
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. 
KNN