ArticleFoods (Basel, Switzerland)2019
Discrimination of Chinese Liquors Based on Electronic Nose and Fuzzy Discriminant Principal Component Analysis.
Article in Foods (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 48 citations in OpenAlex.
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- Non-Destructive Detection of Fruit Quality: Technologies, Applications and Prospects.Foods (Basel, Switzerland) · 2025Review
- Combining UDT with XGBoost to identify the geographical origin of black beans by near-infrared spectroscopy.Current research in food science · 2025Article
- Research on fault prediction and speed control system for unmanned combine harvesters based on IPSO-SVM and fuzzy logic.Frontiers in plant science · 2025Article
- Identifying Variables Influencing Traditional Food Solid-State Fermentation by Statistical Modeling.Foods (Basel, Switzerland) · 2024Article
- Accurate Classification of Chunmee Tea Grade Using NIR Spectroscopy and Fuzzy Maximum Uncertainty Linear Discriminant Analysis.Foods (Basel, Switzerland) · 2023Article
- Classification of Chinese vinegar varieties using electronic nose and fuzzy Foley-Sammon transformation.Journal of food science and technology · 2020Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nose system can quickly detect different types of Chinese liquors according to their aromas. In this study, an e-nose system was designed to identify six types of Chinese liquors, and a novel feature extraction algorithm, called fuzzy discriminant principal component analysis (FDPCA), was developed for feature extraction from e-nose signals by combining discriminant principal component analysis (DPCA) and fuzzy set theory. In addition, principal component analysis (PCA), DPCA, K-nearest neighbor (KNN) classifier, leave-one-out (LOO) strategy and k-fold cross-validation (
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Registered trials
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