ArticleJournal of food science and technology2020
Classification of Chinese vinegar varieties using electronic nose and fuzzy Foley-Sammon transformation.
Article in Journal of food science and technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Research Progress on Flavor Differences and the Formation Mechanism of Traditional Chinese Cereal Vinegar.Foods (Basel, Switzerland) · 2025Review
- Non-Destructive Detection of Fruit Quality: Technologies, Applications and Prospects.Foods (Basel, Switzerland) · 2025Review
- Comparative study of indirect and direct feature extraction algorithms in classifying tea varieties using near-infrared spectroscopy.Current research in food science · 2025Article
- Combining UDT with XGBoost to identify the geographical origin of black beans by near-infrared spectroscopy.Current research in food science · 2025Article
- High-accuracy classification and origin traceability of peanut kernels based on near-infrared (NIR) spectroscopy using Adaboost - Maximum uncertainty linear discriminant analysis.Current research in food science · 2024Article
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Authors and funding
6 authors.
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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,
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