Identification of Dark Tea (Camellia sinensis (L.)) Origins According to Chemical Composition Combined with Bayes Classification Pattern Recognition

Jing-Ming Ning ； Jun-Ting Fang ； Xian-Jingli Luo ； Zheng-Zhu Zhang ； Xiao-Chun Wan

《Advance Journal of Food Science and Technology》 12卷3期 (2016/09) Pp. 150-154

https://doi.org/10.19026/ajfst.12.2872

並列摘要

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.

並列關鍵字

Bayes ； Dark tea ； different origins ； pattern recognition