Abstract
This study utilized linear sweep voltammetry (LSV) coupled with pattern recognition techniques to investigate the electrochemical behavior and chemical composition of various tea types, including green tea, black tea, oolong tea, pu-erh tea, white tea, and floral tea. LSV curves were obtained for each tea type, revealing distinct oxidation peaks at different potentials. The analysis of the LSV data enabled the differentiation of tea types based on their electrochemical behavior, as well as the identification of variations within the same tea type based on different grades. Furthermore, tests were conducted on catechins and alkaloids present in tea leaves, and their ultraviolet wavelength scans were obtained. Liquid chromatography was employed to separate and quantify the 11 compounds of interest. The concentrations of these components were determined in 30 tea leaf samples. Cluster analysis and principal component analysis (PCA) were performed on the LSV data, resulting in the successful grouping and classification of the tea samples. The findings highlight the potential of electrochemical analysis coupled with pattern recognition techniques for the rapid and non-destructive assessment of tea quality. Further research should focus on refining and expanding the capabilities of this approach for broader applications in the food and beverage industry.
Published Version
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