ArticleNPJ science of food2025
Geographical origin discrimination of Chenpi using machine learning and enhanced mid-level data fusion.
Article in NPJ science of food, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Geographical Discrimination of Xinhui Citri Reticulatae Pericarpium by DART-QTOF-MS.Foods (Basel, Switzerland) · 2026Article
- Identification of Citri Reticulatae Pericarpium (Chenpi) From Different Cultivars via LC-MS/MS and UPLC Coupled With Multivariate Chemometrics Analysis.Food science & nutrition · 2026Article
- LC-MS/MS-based metabolomics coupled with machine learning for screening candidate biomarkers in bacon.Food chemistry: X · 2026Article
- Multisource spectral fusion combined with variable selection for rapid geographical origin discrimination ofFrontiers in chemistry · 2025Article
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Authors and funding
9 authors.
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Abstract
Chenpi, or dried tangerine peel, is a traditional Chinese ingredient valued in medicine and edible for its digestive and respiratory benefits. The geographical origin of Chenpi is important, as it can impact its quality, active compounds and market value. This study develops a strategy to distinguish Chenpi samples on its origin. Thirty-nine samples from eight regions in Xinhui district (Guangdong, China) are analyzed by gas chromatography (GC) and mid-infrared (MIR) technique. Four machine learning methods are employed to establish discrimination models based on GC and MIR data, with two mid-level data fusion strategies to combine the data. The results show that data fusion significantly improves Chenpi origin discrimination. The K-nearest neighbors and artificial neural network models, using modified mid-level data fusion, provide the best performance, misclassified only one sample. Machine learning in combination with modified mid-level data fusion strategy provides effective classification of Chenpi samples from different geographical origins.
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