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Rapid identification of the green tea geographical origin and processing month based on near-infrared hyperspectral imaging combined with chemometrics

文献类型： 外文期刊

作者： Liu, Ying 1 ; Huang, Junlan 1 ; Li, Menghui 1 ; Chen, Yuyu 1 ; Cui, Qingqing 1 ; Lu, Chengye 1 ; Wang, Yujie 1 ; Li, Luqing 1 ; Xu, Ze 2 ; Zhong, Yingfu 2 ; Ning, Jingming 1 ;

作者机构： 1.Anhui Agr Univ, State Key Lab Tea Plant Biol & Utilizat, Hefei 230036, Peoples R China

2.Chongqing Acad Agr Sci, Tea Res Inst, Chongqing 402160, Peoples R China

关键词： Geographical origin; Processing month; Green tea; Hyperspectral imaging

期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.3； 五年影响因子：3.8 ）

ISSN： 1386-1425

年卷期： 2022 年 267 卷

页码：

收录情况： SCI

摘要： The geographical origin and processing month of green tea greatly affect its economic value and consumer acceptance. This study investigated the feasibility of combining near-infrared hyperspectral imaging (NIR-HSI) with chemometrics for the identification of green tea. Tea samples produced in three regions of Chongqing (southeastern Chongqing, northeastern Chongqing, and western Chongqing) for four months (from May to August 2020) were collected. Principal component analysis (PCA) was used to reduce data dimensionality and visualize the clustering of samples in different categories. Linear partial least squares-discriminant analysis (PLS-DA) and nonlinear support vector machine (SVM) algorithms were used to develop discriminant models. The PCA-SVM models based on the first four and first five principal components (PCs) achieved the best accuracies of 97.5% and 95% in the prediction set for geographical origin and processing month of green tea, respectively. This study demonstrated the feasibility of HSI in the identification of green tea species, providing a rapid and nondestructive method for the evaluation and control of green tea quality. (c) 2021 Published by Elsevier B.V.

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