Comparison of volatile and nonvolatile metabolites in green tea under hot-air drying and four heat-conduction drying patterns using widely targeted metabolomics.

Hot-air and heat-conduction drying are the most common drying patterns in green tea production. However, the differences between them in terms of the resulting green tea chemical compounds have not been illustrated systematically. In this study, 515 volatile and 204 nonvolatile metabolites were selected to compare the differences between hot-air drying green tea (HAGT) and four heat-conduction drying green teas (HCDGTs) using widely targeted metabolomics. The results showed notable changes in volatile compounds; for example, two kinds of HCDGTs preferred to form chestnut-like and caramel-like key odorants. In addition, 14 flavonol glycosides, 10 catechins, 9 phenolic acids, 8 amino acids, 7 flavonols, and 3 sugars were significantly changed between HAGT and HCDGTs (p < 0.05), presenting a tremendous discrepancy in the transformation of nonvolatile compounds. Our results provide clear guidance for the precise manufacturing of green tea by four common heat-drying patterns and hot air-drying patterns.

[Study on quality evaluation of Dafo Longjing tea based on near infrared spectroscopy].

Seven quantitative analysis models for Dafo Longjing tea, including tea color, liquor color, aroma, taste, infused leaf, total points of five factors and total points of six factors, were built by applying near infrared spectroscopy combined with partial least squares (NIRS-PLS), in order to find a objective and scientific method for tea quality evaluation. Results showed that both the calibration samples and the prediction samples of the seven models had acquired a high fitting degree when the number of principal components was below 10, and the value of Rc, RMSEC, Rp and RMSEP were between 90.48%-98.43%, 1.14%-2.09%, 90.00%-96.65%, and 1.52% 2.84%, respectively. Among them, the total points of five factors model had the best prediction performance, and the value of Rp and RMSEP was 96.65% and 1.52%, respectively. Moreover, it was also found that the prediction precision of total points models were higher than that of single factor ones. It seems that the quality evaluation of Dafo Longjing tea could be realized by using NIRS-PLS to some extent.