Predictive models for sensory score and physicochemical composition of Yuezhou Longjing tea using near-infrared spectroscopy and data fusion

文献类型： 外文期刊

第一作者： Chen, Yong

作者： Chen, Yong;Guo, Mengqi;Guo, Mengqi;Ding, Zezhong;Zhang, Haowen;Lu, Min;Qi, Dandan;Dong, Chunwang;Chen, Kai;Jiang, Xinfeng

作者机构：

关键词： NIR; Data fusion; Physicochemical analysis; Sensory score; Model building

期刊名称：TALANTA （ 影响因子：6.1； 五年影响因子：5.4 ）

ISSN： 0039-9140

年卷期： 2024 年 273 卷

页码：

收录情况： SCI

摘要： 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 nearinfrared spectroscopy and data fusion have proved as feasible.

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