高光谱成像
发酵
红茶
随机森林
预处理器
生物系统
支持向量机
可视化
糖
过程(计算)
钥匙(锁)
计算机科学
食品科学
模式识别(心理学)
人工智能
化学
数学
生物
操作系统
计算机安全
作者
Chongshan Yang,Yan Zhao,Ting An,Zhongyuan Liu,Yongwen Jiang,Yaqi Li,Chunwang Dong
标识
DOI:10.1016/j.lwt.2021.110975
摘要
Fermentation is a key process that affects the quality of black tea. In this study, we discussed the changes and influencing factors of key endoplasmic components at different positions of stacked fermented leaves, and the effects of different preprocessing, variable selection and intelligent algorithm on the model performance are compared, the quantitative prediction model of main endoplasmic components of Congou black tea under different fermentation time series was established, finally, the content distribution is depicted in different colors. The results show that the RPD values of the random forest (RF) prediction model constructed using the optimal variables of theafuscin, thearubigin, catechin, caffeine, and soluble sugar were 3.40, 2.21, 5.71, 1.46, and 2.89, respectively. The RPD values of the support vector machine (SVR) prediction model constructed using the optimal variables of theaflavin and the phenol ammonia ratio were 3.78 and 2.91, respectively. Furthermore, the visualization process successfully displayed the distribution of various quality indicators of the samples at different time periods. These research results lay a theoretical foundation for advancing the judicious processing of black tea.
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