Rapid detection of catechins during black tea fermentation based on electrical properties and chemometrics

Abstract

Catechins are important to evaluate the quality of black tea. In this study, a quantitative prediction model was established based on the measurement of electrical properties and a chemometrics method to detect the catechin content in fermented black tea. The effects of different preprocessing, variable screening methods, and nonlinear algorithms on the model were studied. Results show that the electrical parameters most sensitive to catechin content are equivalent parallel capacitance, loss factor, and reactance mainly at low frequencies (0.05–0.1 kHz). Normalization processing (Zscore), variable combinations' population analysis and the iterative retained information variable algorithm (VCPA-IRIV), and the nonlinear intelligent algorithm random forest (RF) can all effectively improve the performance of a catechin prediction model. In the VCPA-IRIV-RF model, the number of introduced variables was reduced from 162 to 9 with a compression ratio of 94.5%; the root mean square error of prediction and the root mean square error of validation of this model were only 0.269 and 0.214, respectively. The predictive correlation coefficient, correlation coefficient of calibration, and residual predictive deviation increased to 0.988, 0.994, and 5.47, respectively, indicating the good performance of the model. The rapid and nondestructive determination of catechin content in black tea fermentation using a method to detect the electrical properties seems to be practical.