A Combination Of LC-MS Techniques And Chemometrics In Application Of Different Types Of Tea

With the development of social economy and the improvement of people’s living standard, Natural pollution-free food has get more and more attention. As one of the three non-alcoholic beverages, tea has get much people’s love for its significant antioxidant, anti-aging, hypotensive activity, hypolipidemic activity, hypoglycemic activity, prevent cardiovascular disease, weight loss and other pharmacological effects. The main physiological active ingredients are including polyphenols, amino acids, caffeine, tea polysaccharide and so on. Besides, these chemical constituents are also important components constituting the taste of tea, their relative content and proportion determines the taste and quality of tea. The study found that different tea’s chemical composition will be changed accordingly due to different production processes, and then form their own unique quality characteristics. China, as the world’s largest tea producer, tea production process is very diverse. The tea quality often exist differences, this leads to the market often appear as deceptive and shoddy phenomenon which seriously disrupted the market order and damaged the interests of consumers. This paper aims to determine the chemical composition of different types of teaby LC-MS. Combine with multivariate statistical methods, such as principal component analysis, cluster analysis and so on, creation the mode of tea species identification, thereby providing fast and reliable detection method for the identification of tea.This experiment use green tea, white tea, yellow tea, oolong tea, black tea and dark teas samples. Extracted the major chemical components of tea with the method of60%methanol solution ultrasonic extraction, then determine the nature and relative quantitation by LC-MS. The main research contents and conclusions are as follows:(1) The detection of tea biochemical components can mainly be summarized as phenolic acid, catechins, alkaloids, flavonoids and glycosides by their nature, then the content of each substance of the six tea has been significant analysed. The results showed that phenolic acids are greatly influenced by the degree of fermentation. Semi-fermented tea and unfermented tea existed significant difference. Catechin content is also closely related to the tea process. Its content decreased gradually with the enhanced of the degree of fermentation, and show significant changes. Alkaloid content of each tea is closer, indicated alkaloid content was less affected by the tea process. The trend of flavonol and glycosidic content are similar, they are closer in the unfermented tea(such as green tea, white tea and yellow tea); decreased content in the semi-fermented tea; the content in the fermented tea(black tea anddark tea)was significantly reduced, and present a significant difference. This indicated that tea process seriously affecting phenolic acids, catechins, flavonols and glycosides content, and then forming various kinds of unique quality characteristics of tea.(2) Analysis the chemical composition types and content of the tea with the method of principal component analysis and cluster analysis. Results show that principal component analysis for oolong tea, black tea and dark tea has obviously good clustering effect. To the unfermented green tea, white tea, yellow tea, clustering effect is not obvious, overlap together, which show the tea chemical composition is very complicated and three kinds of types and content of the composition of tea contains are close. Only the two principal components analysis is not enough, we need more statistical variables to get better analysis results. According to the results of clustering analysis, different varieties of tea together in the same category, and clustering effect reached100%. Among them, white tea, yellow tea, and then both togetherwith green tea, form the unfermented tea cluster. And then three teas get together with oolong tea, followed with black tea, while black tea in another one, show that tea component has undergone profound changes after the fermentation process.In this experiment,we have determined the chemical components of tea by LC-MS, and combined with statistical analysis method. According to the result of the different varieties of discrimination, both LC-MS combined with principal component analysis and LC-MS combined with clustering analysis all have good effect on the identification of varieties of tea, and the former is superior to the latter with100%accuracy. This study provides a new and effective method for the rapid identification of varieties of tea, but also provides a new train of thoughtfor the identification of the origin and grade of tea, and provide a guarantee for standardize the development of the tea market.