Application of machine learning algorithms in quality assurance of fermentation process of black tea- based on electrical properties
文献类型： 外文期刊
作者： Zhu, Hongkai 1 ; Liu, Fei 4 ; Ye, Yang 1 ; Chen, Lin 1 ; Li, Jingyuan 3 ; Gu, Anhui 1 ; Zhang, Jianqiang 1 ; Dong, Chunwa 1 ;
作者机构： 1.China Acad Agr Sci, Tea Res Inst, Hangzhou 310008, Zhejiang, Peoples R China
2.Minist Agr, Key Lab Tea Biol & Resource Utilizat, Hangzhou 310008, Zhejiang, Peoples R China
3.Univ Copenhagen, Fac Sci, Dept Food Sci, Rolighedsvej 26, DK-1958 Frederiksberg, Denmark
4.Sichuan Acad Agr Sci, Tea Res Inst, Chengdu 610066, Sichuan, Peoples R China
关键词： Black tea; Fermentation; Electrical properties; Quality components; Random forest; Machine learning algorithms
期刊名称：JOURNAL OF FOOD ENGINEERING （ 影响因子：5.354； 五年影响因子：5.144 ）
ISSN： 0260-8774
年卷期： 2019 年 263 卷
页码：
收录情况： SCI
摘要： Fermentation process directly determines the product quality of black tea. This work aimed to develop a rapid method for detecting the degree of fermentation of black tea based on electrical properties of tea leaves. An LCR meter employed to identify 11 electrical parameters of tea leaves during the fermentation process, and the content of catechins and tea pigments in tea leaves were measured by using HPLC and UV-Vis spectrometer, respectively. Principal component analysis and hierarchical clustering analysis applied to divide samples into different groups in the degree of fermentation. Correlation analysis used to characterize the responding strength of electrical parameters on the variation of catechins and pigments. Finally, multilayer perceptron, random forest, and support vector machine algorithm used to build discrimination models of fermentation degree, and the average accuracy rate on the testing set reached to 88.90%, 100%, and 76.92%, respectively.
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