FOOD SCIENCE ›› 2019, Vol. 40 ›› Issue (10): 214-219.doi: 10.7506/spkx1002-6630-20180604-028

• Composition Analysis • Previous Articles     Next Articles

Qingzhuan Brick Tea Infusion: Analysis of Taste Components and Establishment of Quality Evaluation Model

WANG Shengpeng, GONG Ziming*, ZHENG Pengcheng, LIU Panpan, GAO Shiwei, TENG Jing, WANG Xueping, YE Fei, ZHENG Lin, GUI Anhui   

  1. Institute of Fruit and Tea, Hubei Academy of Agricultural Sciences, Wuhan 430064, China
  • Online:2019-05-25 Published:2019-05-31

Abstract: In the present experiment, Qingzhuan tea samples from different manufacturers were used. Taste sensory evaluation of the tea infusions was carried out, followed by chemical measurement. Then, representative chemical components were selected by principal component analysis (PCA) and the correlation coefficient method. Finally, a regression equation for taste quality evaluation of tea infusion was established by principal component regression (PCR) or stepwise regression (SR), and the predictive performance of the regression equation was tested by applying it to unknown samples. The results showed that five components were found to be closely related to the taste quality of tea infusion (P < 0.05), whose contributions to the taste of tea infusion were in the descending order of EGCG, total catechins, tea polyphenols, ECG and EGC. The SR model was better than the PCR model, and the correlation coefficient of calibration (Rc 2 ), root mean square error of cross-validation (RMSECV), correlation coefficient of prediction (Rp 2) and root mean square error of prediction (RMSEP) of the SR model were 0.973 5, 0.380 7, 0.968 1 and 0.400 0, respectively. The SR model showed better predictive performance for the unknown samples (Rp 2 = 0.974 6, and RMSEP = 0.391 5). The results showed that the chemometric model was able to predict the taste quality of Qingzhuan brick tea accurately and reliably and could provide a new method for evaluating the taste quality of Qingzhuan tea infusion.

Key words: Qingzhuan tea, tea infusion, regression equation, principal component analysis, correlation coefficient

CLC Number: