数字农科院2.0

Detection of anthocyanin content in fresh Zijuan tea leaves based on hyperspectral imaging

文献类型： 外文期刊

作者： Fushuang Dai;Jiang Shi;Chongshan Yang;Yang Li;Yan Zhao;Zhongyuan Liu;Ting An;Xiaoli Li;Peng Yan;Chunwang Dong

作者机构：

关键词： Anthocyanin content;Chemometric method;Filtering of variables;Hyperspectral imaging;Tenderness;Zijuan tea

期刊名称： Food Control

ISSN： 0956-7135

年卷期： 2023 年 152 卷

页码：

收录情况： SCIE（2023版）

摘要： Anthocyanins are characteristic substances that determine the leaf color and sensory quality of Zijuan tea. The qualitative determination of anthocyanin content in fresh Zijuan tea leaves by artificial vision may lead to the uneven quality of tea products. Hyperspectral images at 400–956 nm were obtained. K-nearest neighbor (KNN) and support vector machine (SVM) models were used to identify the tenderness grade, and partial least squares regression (PLSR) and support vector regression (SVR) models were established based on principal component analysis for prediction. In short, the classification accuracy of the SVM model was better than 90%; variable combination population analysis (VCPA) and variables combination population analysis combined with iterative retained information variable (VCPA-IRIV) can effectively simplify the model and the prediction accuracy is over than 0.92. The best predictive models of total anthocyanins, Cya-3,5-O-diglucoside, Cya-3-O-glucoside, and petunidin were VCPA + SVR, VCPA-IRIV + SVR, VCPA-IRIV + PLSR, and VCPA + PLSR, and the relative percent deviation were 3.233, 2.868, 3.529 and 3.298, respectively. The total anthocyanins were visualized by the spatial distribution of anthocyanins in samples with different tenderness. This study provided a rapid nondestructive method of fresh Zijuan tea leaf tenderness grade and quality components to control the quality of fresh leaf picking of tea with specific leaf color.

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