Qualitative and quantitative analysis of the pile fermentation degree of Pu-erh tea.

During the pile fermentation (PF) process, changes to the polyphenol composition directly affect the quality of Pu-erh tea. In this study, we have applied laboratory-made computer vision system (CVS) and miniature near-infrared spectroscopy (NIRS) to the at-line rapid detection of the PF degree of Pu-erh tea at an industrial scale. High-performance liquid chromatography was used for determining the content of catechins (EGCG, EGC ...) and gallic acid (GA). Based on the least-square support vector machine (LSSVM) qualitative model analysis, the texture features and color information extracted by CVS better predicted the degree of PF with a prediction set of 99.30% and calibration set of 100.00% compared to the spectral information extracted by NIRS. The best quantitative models for total catechins (TC), GA/TC, and red and green values of the tea infusion (R-TI, G-TI) were obtained with residual prediction deviations (RPD) of 4.76, 2.36, 5.18, and 4.71, respectively, based on CVS fusion data. And the optimal PF degree of Pu-erh tea was defined when the predicted values of TC, GA/TC, R-TI, and G-TI were in the ranges of 0.46 ± 0.08 mg/g, 19.04 ± 6.67, 74.81 ± 6.37, and 29.81 ± 2.46, respectively. In-situ quality monitoring of Pu-erh tea PF was realized. • CVS and NIRS were first applied to predict the quality changes of Pu-erh tea. • The CVS recognition rate was higher than NIRS up to 100% by LSSVM model. • TC, GA and tea infusion color were identified as quality prediction indicators. • The pile fermentation degree was defined based on quality prediction indicators. • "Color card" standards for different degree of pile fermentation were created. [ABSTRACT FROM AUTHOR]