In this study, NIR quantitative prediction model was established for sensory score and physicochemical components of different varieties and quality grades of Yuezhou Longjing tea. Firstly, L, a, b color factors and diffuse reflection spectral data are collected for each sample. Subsequently, the original spectrum is preprocessed. Three techniques for selecting variables, CARS, BOSS, and SPA, were utilized to extract optimal feature bands. Finally, the spectral data extracted from feature bands were fused with L, a and b color factors to build SVR and PLSR prediction models. enabling the rapid non-destructive discrimination of different varieties and grades of Yuezhou Longjing tea. The outcomes demonstrated that BOSS was the best variable selection technique for sensory score and the distinctive caffeine wavelengths, CARS, however, was the best variable selection technique for catechins distinctive wavelengths. Additionally, the middle-level data fusion-based non-linear prediction models greatly outperformed the linear prediction models. For the prediction models of sensory score, catechins, and caffeine, the relative percent deviation (RPD) values were 2.8, 1.6, and 2.6, respectively, suggesting the good predictive ability of the models. In conclusion, evaluating the quality of the five Yuezhou Longjing tea varieties using near-infrared spectroscopy and data fusion have proved as feasible.

中文翻译：

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利用近红外光谱和数据融合预测岳州龙井茶感官评分和理化成分

本研究建立了不同品种、不同品质等级岳州龙井茶感官评分和理化成分的近红外定量预测模型。首先，收集每个样品的L、a、b颜色因子和漫反射光谱数据。随后，对原始光谱进行预处理。利用 CARS、BOSS 和 SPA 三种选择变量的技术来提取最佳特征带。最后，将从特征波段提取的光谱数据与L、a和b颜色因子融合，构建SVR和PLSR预测模型。实现对越州龙井茶不同品种、等级的快速无损判别。结果表明，BOSS 是感官评分和独特咖啡因波长的最佳变量选择技术，而 CARS 是儿茶素独特波长的最佳变量选择技术。此外，基于中层数据融合的非线性预测模型大大优于线性预测模型。对于感官评分、儿茶素和咖啡因的预测模型，相对百分比偏差（RPD）值分别为2.8、1.6和2.6，表明模型具有良好的预测能力。综上所述，利用近红外光谱和数据融合评价5个岳州龙井茶品种的品质是可行的。