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

TY - JOUR

T1 - Application of machine learning algorithms in quality assurance of fermentation process of black tea

T2 - based on electrical properties

AU - Zhu, Hongkai

AU - Liu, Fei

AU - Ye, Yang

AU - Chen, Lin

AU - Liu, Jingyuan

AU - Gui, Anhui

AU - Zhang, Jianqiang

AU - Dong, Chunwang

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Black tea

KW - Electrical properties

KW - Fermentation

KW - Machine learning algorithms

KW - Quality components

KW - Random forest

U2 - 10.1016/j.jfoodeng.2019.06.009

DO - 10.1016/j.jfoodeng.2019.06.009

M3 - Journal article

AN - SCOPUS:85067558468

SN - 0260-8774

VL - 263

SP - 165

EP - 172

JO - Journal of Food Engineering

JF - Journal of Food Engineering

ER -