数字农科院2.0

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

文献类型： 外文期刊

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

作者机构：

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

期刊名称： Journal of Food Composition and Analysis

ISSN： 0889-1575

年卷期： 2023 年 123 卷

页码：

收录情况： SCIE（2023版）

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.

分类号：

  • 相关文献

[1]Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning. Yi Ming Cao,Yan Zhang,Shuang Ting Yu,Kai Kuo Wang,Ying Jie Chen,Zi Ming Xu,Zi Yao Ma,Hong Lu Chen,Qi Wang,Ran Zhao,Xiao Qing Sun,Jiong Tang Li. 2023

[2]Integration of hyperspectral imaging, non-targeted metabolomics and machine learning for vigour prediction of naturally and accelerated aged sweetcorn seeds. Tingting Zhang,Long Lu,Ni Yang,Ian D. Fisk,Wensong Wei,Li Wang,Jing Li,Qun Sun,Rensen Zeng. 2023

[3]Applications of a Hyperspectral Imaging System Used to Estimate Wheat Grain Protein: A Review. Junjie Ma,Bangyou Zheng,Yong He. 2022

[4]Skin hyperspectral imaging and machine learning to accurately predict the muscular poly-unsaturated fatty acids contents in fish. Yi Ming Cao,Yan Zhang,Qi Wang,Ran Zhao,Mingxi Hou,Shuang Ting Yu,Kai Kuo Wang,Ying Jie Chen,Xiao Qing Sun,Shijing Liu,Jiong Tang Li. 2024

[5]Prediction of Vigor of Naturally Aged Seeds from Xishuangbanna Cucumber (Cucumis sativus L. var. xishuangbannanesis) Using Hyperspectral Imaging. Zhang, Meng,Song, Jiangping,Jia, Huixia,Zhang, Xiaohui,Yang, Wenlong,Wang, Yang,Wang, Haiping. 2025

[6]Visible-near infrared hyperspectral imaging for non-destructive estimation of leaf nitrogen content under water-saving irrigation in protected tomato cultivation. Hu, Caixia,Zhao, Tingting,Duan, Yingying,Zhang, Yungui,Wang, Xinxiu,Li, Jie,Zhang, Guilong. 2025

[7]Parameter optimization in soluble solid content prediction of entire bunches of grape based on near infrared spectroscopic technique. Yu, Jing,Sun, Xiangyu,Huang, Weidong,Wang, Hui.

[8]Nondestructive detection of moldy chestnut based on near infrared spectroscopy. Liu, J.,Li, X. Y.,Wang, W.,Zhang, J.,Zhang, R.,Liu, P.,Li, P. W.,Zhang, J.. 2010

[9]Nondestructive detection of moldy chestnut based on near infrared spectroscopy. Liu, J.,Li, X. Y.,Wang, W.,Zhang, J.,Zhang, R.,Liu, P.,Li, P. W.,Zhang, J.. 2010

[10]Quantitative prediction and visual detection of the moisture content of withering leaves in black tea (Camellia sinensis) with hyperspectral image. Chunwang Dong,Ting An,Ming Yang,Chongshan Yang,Zhongyuan Liu,Yang Li,Dandan Duan,Shuxiang Fan. 2022

[11]Nondestructive Detection and Quality Grading System of Walnut Using X-Ray Imaging and Lightweight WKNet. Xiangpeng Fan,Jianping Zhou. 2025

[12]Brassinosteroids Improve Quality of Summer Tea (Camellia sinensis L.) by Balancing Biosynthesis of Polyphenols and Amino Acids. Li, Xin,Ahammed, Golam J.,Li, Zhi-Xin,Zhang, Lan,Wei, Ji-Peng,Shen, Chen,Yan, Peng,Zhang, Li-Ping,Han, Wen-Yan,Ahammed, Golam J.,Li, Zhi-Xin,Shen, Chen. 2016

[13]The Tea Weevil, Myllocerinus aurolineatus, is Attracted to Volatiles Induced by Conspecifics. Sun, Xiao-Ling,Wang, Guo-Chang,Jin, Shan,Gao, Yu,Chen, Zong-Mao,Wang, Guo-Chang,Cai, Xiao-Ming.

[14]Prediction of Chinese green tea ranking by metabolite profiling using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS). Jing, Jin,Wang, Jie,Jing, Jin,Shi, Yuanzhi,Zhang, Qunfeng,Wang, Jie,Ruan, Jianyun,Shi, Yuanzhi,Zhang, Qunfeng,Ruan, Jianyun.

[15]Effect of potassium, magnesium and sulphur applied in different forms of fertilisers on free amino acid content in leaves of tea (Camellia sinensis L). Ruan, JY,Wu, X,Ye, Y,Hardter, R. 1998

[16]Exogenous Melatonin Improves Tea Quality Under Moderate High Temperatures By Increasing Epigallocatechin-3-Gallate And Theanine Biosynthesis In Camellia Sinensis L.. Li, X, Li, MH, Deng, WW, Ahammed, GJ, Wei, JP, Yan, P, Zhang, LP, Fu, JY, Han, WY. 2020

[17]Impact of N application rate on tea (Camellia sinensis) growth and soil bacterial and fungi communities. Tang, Sheng,Zhou, Jingjie,Pan, Wankun,Tang, Rui,Ma, Qingxu,Xu, Meng,Qi, Tong,Ma, Zhengbo,Fu, Haoran,Wu, Lianghuan. 2022

[18]Effects of Preharvest Shading on Dynamic Changes in Metabolites, Gene Expression, and Enzyme Activity of Three Tea Types during Processing. Shao, Chenyu,Deng, Zhiying,Liu, Jie,Li, Yunfei,Zhang, Chenyu,Yao, Suhang,Zuo, Haoming,Shi, Yue,Yuan, Shijie,Qin, Lijuan,Liu, Zhonghua,Shen, Chengwen. 2022

[19]Hormonal regulation of health-promoting compounds in tea (Camellia sinensis L.). Golam Jalal Ahammed,Xin Li. 2022

[20]Epigallocatechin gallate (EGCG) nanoselenium application improves tea quality (Camellia sinensis L.) and soil quality index without losing microbial diversity: A pot experiment under field condition. Xiangchun Zhang,Xiangde Yang,Jianyun Ruan,Hongping Chen. 2024

作者其他论文 更多>>