Submitted to: Food Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 10, 2010
Publication Date: November 16, 2010
Citation: Zhao, Y., Chen, P., Lin, L., Harnly, J.M. 2011. Identification, quantitation, and comprehensive assessment of green, white, and pu-erh teas using UPLC/UV/MS. Food Chemistry. 126:1269-1277. Interpretive Summary: Tea (Camellia sinensis L.) has been used as an important drink for over 1000 years and frequently serves as a traditional medicine due to potential health benefits. It is one of the most widely consumed beverages in the world, next to water. To assess the qualities of different teas (green pu-erh, green tea, and white tea), a simple, fast, and efficient analytical and detection method was developed. Sixty-eight bioactive compounds were identified in the teas, and 54 could be quantified using the method. Chemical differences between different kinds of teas were noted. This is an example of using chemometric analyses (principal components analysis (PCA) and hierarchical clustering analysis (HCA)) to classify and differentiate the tea samples.
Technical Abstract: Tea (Camellia sinensis L.), an important drink and a traditional medicine for thousands of years, contains many compounds of potential benefit to health. Growing season, geographic region, and fermentation method create many variations in tea composition, which contributes to the unique characteristics of each tea. In this study, a simple, rapid, and efficient ultra-performance liquid chromatography (UPLC) method combined with diode array detector (DAD), mass spectroscopic (MS) detection, and chemometrics analysis was used to analyze three different types of teas (green pu-erh, green tea, white tea). Using the developed method, 68 compounds were identified and 54 were quantified based on retention times, UV spectra, and MS spectra by referencing to available standards and data in the literature. The results showed the chemical differences between of the tested teas. Principal components analysis (PCA) was applied to classify and distinguish between tea samples.