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

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 extractio...

Full description

Saved in:
Bibliographic Details
Published in:Journal of food science and technology 2020-04, Vol.57 (4), p.1310-1319
Main Authors: Wu, Xiao-Hong, Zhu, Jin, Wu, Bin, Huang, Da-Peng, Sun, Jun, Dai, Chun-Xia
Format: Article
Language:English
Subjects:
Algorithms
Brewing
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Classification
Discriminant analysis
Electronic noses
Feature extraction
Flavors
Food Science
Fuzzy systems
Genetic transformation
Information processing
Nutrition
Original
Original Article
Principal components analysis
Raw materials
Smell
Transformations
Vinegar
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0022-1155
0975-8402
DOI:10.1007/s13197-019-04165-y