Monitoring the major taste components during black tea fermentation using multielement fusion information in decision level
文献类型： 外文期刊
作者： An, Ting 1 ; Wang, Zheli 3 ; Li, Guanglin 1 ; Fan, Shuxiang 3 ; Huang, Wenqian 3 ; Duan, Dandan 4 ; Zhao, Chunjiang 1 ; Tian, Xi 3 ; Dong, Chunwang 2 ;
作者机构： 1.Southwest Univ, Coll Engn & Technol, Chongqing 400715, Peoples R China
2.Shandong Acad Agr Sci, Tea Res Inst, Jinan 250033, Peoples R China
3.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China
4.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China
关键词： Black tea; Hyperspectral imaging; Electrical properties; Data fusion; Stacking combination strategy
期刊名称：FOOD CHEMISTRY-X （ 影响因子：6.1； 五年影响因子：6.4 ）
ISSN： 2590-1575
年卷期： 2023 年 18 卷
页码：
收录情况： SCI
摘要： Hitherto, the intelligent detection of black tea fermentation quality is still a thought-provoking problem because of one-side sample information and poor model performance. This study proposed a novel method for the prediction of major chemical components including total catechins, soluble sugar and caffeine using hyperspectral imaging technology and electrical properties. The multielement fusion information were used to establish quantitative prediction models. The performance of model using multielement fusion information was better than that of model using single information. Subsequently, the stacking combination model using fusion data combined with feature selection algorithms for evaluating the fermentation quality of black tea. Our proposed strategy achieved better performance than classical linear and nonlinear algorithms, with the correlation coefficient of the prediction set (Rp) for total catechins, soluble sugar and caffeine being 0.9978, 0.9973 and 0.9560, respectively. The results demonstrated that our proposed strategy could effectively evaluate the fermentation quality of black tea.
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