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The Analysis and Rapid Non-Destructive Evaluation of Yongchuan Xiuya Quality Based on NIRS Combined with Machine Learning Methods

文献类型： 外文期刊

作者： Zang, Ying 1 ; Wang, Jie 1 ; Wu, Xiuhong 1 ; Chang, Rui 1 ; Wang, Yi 1 ; Luo, Hongyu 1 ; Zhong, Yingfu 1 ; Wu, Quan 1 ; Chen, Zhengming 1 ; Deng, Min 1 ;

作者机构： 1.Chongqing Acad Agr Sci, Inst Tea, Chongqing 402160, Peoples R China

关键词： Yongchuan Xiuya; quality evaluation; near infrared spectroscopy; principal component analysis; artificial neural network

期刊名称：PROCESSES （ 影响因子：2.8； 五年影响因子：3.0 ）

ISSN：

年卷期： 2023 年 11 卷 9 期

页码：

收录情况： SCI

摘要： This paper attempts to analyze and assess Yongchuan Xiuya tea quality quickly, accurately, and digitally. The sensory evaluation method was first used to assess Yongchuan Xiuya tea quality, and then near infrared spectroscopy (NIRS) was obtained, and standard methods were applied to the testing of the chemical components. Next, principal component analysis (PCA) and the correlation coefficient method were used to comprehensively screen out the representative components. Finally, NIRS combined with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN) methods were applied to build quality evaluation models for Yongchuan Xiuya tea, respectively, and external samples were employed to examine the practical application results of the best model. The cumulative variance contribution rate of the first three principal components of the ingredients in tea was 97.73%. Seven components closely related to tea quality were screened out, namely, amino acids, total catechin, epigallocatechin gallate (EGCG), tea polyphenols, water extracts, epicatechin gallate (ECG), and epigallocatechin (EGC) (p < 0.01). Between the two models established to predict the tea quality, the model built by the PLS method had the better results, whose coefficient of determination of prediction (Rp2) and root mean square error of prediction (RMSEP) were 0.7955 and 1.2263, respectively, and the best results were obtained by the nonlinear BP-ANN model, whose R-p(2) and RMSEP were 0.9315 and 0.6787, respectively. The 10 external Yongchuan Xiuya samples were employed to test the best BP-ANN model, and the results of R-2 and RMSEP were 0.9579 and 0.6086, respectively, meaning that the model has good robustness. Therefore, the model established by NIRS combined with the BP-ANN method can be used to assess Yongchuan Xiuya tea quality rapidly, accurately, and digitally, and it can also provide new ideas and methods for evaluating the quality of other teas.

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