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

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...

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Bibliographic Details
Published in:Journal of food engineering Vol. 263; pp. 165 - 172
Main Authors: Zhu, Hongkai, Liu, Fei, Ye, Yang, Chen, Lin, Liu, Jingyuan, Gui, Anhui, Zhang, Jianqiang, Dong, Chunwang
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.12.2019
Subjects:
Black tea
cluster analysis
Electrical properties
Fermentation
flavanols
high performance liquid chromatography
leaves
Machine learning algorithms
neural networks
principal component analysis
product quality
Quality components
quality control
Random forest
rapid methods
support vector machines
ultraviolet-visible spectroscopy
Machine learning algorithms
Random forest
Electrical properties
Black tea
Fermentation
Quality components
ISSN:0260-8774, 1873-5770
Online Access:Get full text
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Summary: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. •Electrical properties used to optimize the fermentation of black tea.•Hierarchical clustering provided an objective classification for fermented samples.•High correlations have been found between electrical properties and catechins.•Random forest can effectively distinguish the degree of fermented samples.
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ISSN:0260-8774
1873-5770
DOI:10.1016/j.jfoodeng.2019.06.009