{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T04:27:31Z","timestamp":1727065651191},"reference-count":29,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T00:00:00Z","timestamp":1638403200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31972466"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Agricultural Sciences","award":["1610212016018"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.<\/jats:p>","DOI":"10.3390\/s21238051","type":"journal-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T07:56:14Z","timestamp":1638431774000},"page":"8051","source":"Crossref","is-referenced-by-count":9,"title":["Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-8140-1022","authenticated-orcid":false,"given":"Chunwang","family":"Dong","sequence":"first","affiliation":[{"name":"Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China"}]},{"given":"Chongshan","family":"Yang","sequence":"additional","affiliation":[{"name":"Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China"},{"name":"College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China"}]},{"given":"Zhongyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China"},{"name":"College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China"}]},{"given":"Rentian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China"},{"name":"College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China"}]},{"given":"Peng","family":"Yan","sequence":"additional","affiliation":[{"name":"Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China"}]},{"given":"Ting","family":"An","sequence":"additional","affiliation":[{"name":"Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China"},{"name":"College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China"}]},{"given":"Yan","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109216","DOI":"10.1016\/j.lwt.2020.109216","article-title":"Intelligent evaluation of black tea fermentation degree by FT-NIR and computer vision based on data fusion strategy","volume":"125","author":"Jin","year":"2020","journal-title":"LWT"},{"key":"ref_2","first-page":"3422","article-title":"Rapid and Dynamic Determination Models of Amino Acids and Catechins Concentrations during the Processing Procedures of Keemun Black Tea","volume":"35","author":"Ning","year":"2015","journal-title":"Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103278","DOI":"10.1016\/j.vibspec.2021.103278","article-title":"Fermentation quality evaluation of tea by estimating total catechins and theanine using near-infrared spectroscopy","volume":"115","author":"Chen","year":"2021","journal-title":"Vib. 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