Tea presents a diverse phenolic composition which is responsible for its alleged biological activities, including the in vivo and in vitro antioxidant capacity. It is very usual to find researches applying statistical methods, such as analysis of variances (ANOVA) and linear Pearson correlation coefficients to analyze the strength of correlation between phenolic composition and the in vitro antioxidant activity of teas from Camellia sinensis (green, black, white, oolong, red, and yellow teas) and Ilex paraguariensis (Yerba-mate), which are the most produced and consumed types of teas. However, evidence has shown that these approaches are not as suitable as multivariate statistical methods once they do not depict nor show association among all results and variables simultaneously, making it difficult to understand clearly the data structure and patterns. Then, the objective of this work is to review and explain some univariate/bivariate and multivariate statistical techniques used to assess the association between phenolic compounds and the in vitro antioxidant activity of green, white, black, red, yellow, oolong and Yerba-mate teas. Moreover, this paper provides an overview on some assays used to estimate the in vitro antioxidant capacity of teas.
Keywords: Correlation analysis; PCA; Yerba-mate; chemometrics; cluster analysis; flavonoids.