Application of machine learning algorithms in quality assurance of fermentation process of black tea: based on electrical properties

Research output: Contribution to journalJournal articleResearchpeer-review

Hongkai Zhu, Fei Liu, Yang Ye, Lin Chen, Jingyuan Liu, Anhui Gui, Jianqiang Zhang, Chunwang Dong

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.

Original languageEnglish
JournalJournal of Food Engineering
Volume263
Pages (from-to)165-172
Number of pages8
ISSN0260-8774
DOIs
Publication statusPublished - 2019

    Research areas

  • Black tea, Electrical properties, Fermentation, Machine learning algorithms, Quality components, Random forest

ID: 226121554