标题：Identification of Dark Tea ( Camellia sinensis (L.)) Origins According to Chemical Composition Combined with Bayes Classification Pattern Recognition
期刊名称：Advance Journal of Food Science and Technology
印刷版ISSN：2042-4868
电子版ISSN：2042-4876
出版年度：2016
卷号：12
期号：3
页码：150-154
DOI：10.19026/ajfst.12.2872
出版社：MAXWELL Science Publication
摘要：As one of the six major teas in China, dark tea is mainly produced in Yunnan, Hunan, Sichuan, Hubei and Guangxi provinces of china. At present, identification geographical of teas mainly depends on the sensory evaluation, because of lacking the quantitative discriminate method. In this study, 38 dark teas were taken, which were collected from five regions. And the main chemical compositions of tea samples were detected according to international standard. Using SPSS18.0 statistical software to reduction dimension, then chose four compositions (GA, EGC, caffeine, total catechins) as the principal component factors, by using Bayes discriminate analysis method, we established the quantitative discriminate model, which could identify the dark teas from different regions. The results show that the Bayes discriminate analysis can be used to discriminate the 38 samples from five regions and the correct rate could be reached 100%, which means the methods established is reliable.
