The study of fingerprint characteristics of Dayi Pu-erh tea using a fully automatic HS-SPME/GC-MS and combined chemometrics method

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Abstract

? 2014 Lv et al.The quality of tea is presently evaluated by the sensory assessment of professional tea tasters, however, this approach is both inconsistent and inaccurate. A more standardized and efficient method is urgently needed to objectively evaluate tea quality. In this study, the chemical fingerprint of 7 different Dayi Pu-erh tea brands and 3 different Ya'an tea brands on the market were analyzed using fully automatic headspace solid-phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS). A total of 78 volatiles were separated, among 75 volatiles were identified by GC-MS in seven Dayi Pu-erh teas, and the major chemical components included methoxyphenolic compounds, hydrocarbons, and alcohol compounds, such as 1,2,3-trimethoxybenzene, 1,2,4-trimethoxybenzene, 2,6,10,14-tetramethyl-pentadecane, linalool and its oxides, ?-terpineol, and phytol. The overlapping ratio of peaks (ORP) of the chromatogram in the seven Dayi Pu-erh tea samples was greater than 89.55%, whereas the ORP of Ya'an tea samples was less than 79.10%. The similarity and differences of the Dayi Pu-erh tea samples were also characterized using correlation coefficient similarity and principal component analysis (PCA). The results showed that the correlation coefficient of similarity of the seven Dayi Pu-erh tea samples was greater than 0.820 and was gathered in a specific area, which showed that samples from different brands were basically the same, despite have some slightly differences of chemical indexes was found. These results showed that the GC-MS fingerprint combined with the PCA approach can be used as an effective tool for the quality assessment and control of Pu-erh tea.

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Lv, S., Wu, Y., Zhou, J., Lian, M., Li, C., Xu, Y., … Meng, Q. (2014). The study of fingerprint characteristics of Dayi Pu-erh tea using a fully automatic HS-SPME/GC-MS and combined chemometrics method. PLoS ONE, 9(12). https://doi.org/10.1371/journal.pone.0116428

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