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 -