Traceability of Rizhao green tea origin based on multispectral data fusion strategy and chemometrics

作者：Mengqi Guo, Zhiwei Chen, Zezhong Ding, Dewen Wang, Dandan Qi, Min Lü, Mei Wang, Chunwang Dong · 发表于：Food Chemistry X · 年份：2025 · DOI：10.1016/j.fochx.2025.102346 · 被引用次数：10 · 研究领域：Spectroscopy and Chemometric Analyses、Advanced Chemical Sensor Technologies、Tea Polyphenols and Effects

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. • Revealed sensory-physicochemical component correlations in Rizhao green tea. • Rizhao green tea's key components surpass southern China's low-latitude teas. • NIR outperforms VIS in Rizhao tea origin discrimination accuracy. • Multi-spectral data fusion enhances predictive model performance.