ArticleFood chemistry: X2025
Traceability of Rizhao green tea origin based on multispectral data fusion strategy and chemometrics.
Article in Food chemistry: X, 2025. 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.
- Geographical Origin Traceability of Tea (Foods (Basel, Switzerland) · 2026Review
- Hyperspectral imaging and machine learning for rapid sensing and visualized retrieval of soil nutrients in high-latitude tea-growing regions.Frontiers in plant science · 2026Article
- Metagenomic insights into rhizosphere microbial communities and functional gene profiles associated with the responses of tea yield and quality to nitrogen-zinc co-fertilization.Frontiers in plant science · 2026Article
- Guizhou Green Tea Metabolomics Analysis: Volatile Metabolites Based on HS-GC-IMS and UPLC-Q-TOF-MSMS and Quality Evaluation.International journal of food science · 2026Article
- Assessment of Tenderness and Anthocyanin Content in Zijuan Tea Fresh Leaves Using Near-Infrared Spectroscopy Fused with Visual Features.Foods (Basel, Switzerland) · 2025Article
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Authors and funding
8 authors.
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Abstract
This study proposes a novel method that combines multispectral data fusion strategies with chemometric analysis for the origin traceability of Rizhao green tea. The research found significant differences in the sensory scores and key physicochemical components (catechins, caffeine, and amino acid content) between Rizhao green tea and tea from southern China. By integrating data from near-infrared and hyperspectral technologies, the prediction accuracy of multivariate models (including Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), and Convolutional Neural Networks (CNN)) was improved. The performance of the fused dataset outperformed single-spectral datasets. The study found significant spectral differences in tea samples from different regions, leading to robust differentiation. Both SVM and RF discriminant models based on near-infrared spectral data achieved 100 % accuracy. This method provides a reliable and efficient tool for green tea traceability, with potential applications in quality control and authenticity verification within the tea industry.
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