Classification of Chinese vinegar varieties using electronic nose and fuzzy Foley-Sammon transformation
| Autor: | Jin Zhu, Bin Wu, Xiaohong Wu, Da-Peng Huang, Chunxia Dai, Jun Sun |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Electronic nose
business.industry 010401 analytical chemistry Pattern recognition 04 agricultural and veterinary sciences Linear discriminant analysis 040401 food science 01 natural sciences Fuzzy logic 0104 chemical sciences k-nearest neighbors algorithm 0404 agricultural biotechnology Discriminant Principal component analysis Classifier (linguistics) Original Article Data pre-processing Artificial intelligence business Food Science Mathematics |
| Zdroj: | J Food Sci Technol |
| ISSN: | 0022-1155 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink