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A nondestructive method for determination of green tea quality by hyperspectral imaging

文献类型： 外文期刊

作者： Tang, Yu 1 ; Wang, Fan 1 ; Zhao, Xiaoqing 1 ; Yang, Guijun 3 ; Xu, Bo 3 ; Zhang, Ying 5 ; Xu, Ze 5 ; Yang, Haibin 5 ; Yan, Lei 1 ; Li, Long 2 ;

作者机构： 1.Beijing Forestry Univ, Sch Technol, Beijing 100083, Peoples R China

2.Chinese Acad Agr Sci, Key Lab Agroprod Proc, Minist Agr & Rural Affairs, Inst Food Sci & Technol,Key Lab Agroprod Proc, Beijing 100193, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

4.Qingyuan Smart Agr & Rural Res Inst, Qingyuan 511500, Peoples R China

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

关键词： Hyperspectral imaging; Tea quality; Nondestructive detection; Machine learning; Visualization

期刊名称：JOURNAL OF FOOD COMPOSITION AND ANALYSIS （ 影响因子：4.3； 五年影响因子：4.6 ）

ISSN： 0889-1575

年卷期： 2023 年 123 卷

页码：

收录情况： SCI

摘要： This study utilizes hyperspectral imaging technology to capture spectral images of three grades of green tea. Reflectance spectra of the three quality grades of tea, namely Grade A, Grade B, and Grade C, were collected between 370 nm and 1040 nm. Three methods, namely Savitzky-Golay (SG) smoothing, standard normal variable (SNV), and first derivative (FD), were employed to preprocess the raw spectra. Dimensionality reduction and visual display were accomplished using t-distributed stochastic neighbor embedding, and the preprocessed spectra with the most optimal visualization effect were selected to extract the key wavelengths using principal components analysis (PCA). The extracted key bands could aid in achieving the detection of tea quality grades, as tea polyphenols are more sensitive between 650 and 800 nm, where 664 nm and 765 nm represent the key wavelengths of catechins. K-nearest neighbor (KNN) and support vector machine (SVM) discriminative models were deployed to model the key wavelengths. The model built by FD-PCA-SVM exhibited the best discriminant effect, with an accuracy of 93.8 % in the training set and 98.2 % in the test set. FD-PCA-SVM discriminant model was used to identify and visualize the tea quality grades with good results. Hyperspectral imaging technology is well-suited to identifying the quality of green tea.

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