A comparative study of the phenolic compounds and the in vitro antioxidant activity of different Brazilian teas using multivariate statistical techniques

Acácio Antonio Ferreira Zielinski, Charles Windson Isidoro Haminiuk, Aline Alberti, Alessandro Nogueira, Ivo Mottin Demiate, Daniel Granato

Research output: Contribution to journalArticlepeer-review

Abstract

A total of 51 Brazilian teas from the species Camellia sinensis, Peumus boldus, Matricaria recutita, Baccharis trimera, Cymbopogon citratus, Pimpinella anisum, Mentha piperita, and Ilex paraguariensis were analyzed in terms of phenolic compounds, color and in vitro antioxidant capacity using ferric reducing antioxidant power (FRAP) and 1,1-diphenyl-2-picrylhydrazyl (DPPH) assays. Data were processed using principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). Significant (P<. 0.01) correlations between antioxidant activity measured by DPPH and FRAP assays with the total phenolic compounds (r = 0.87; r = 0.90, respectively) and flavonoids (r = 0.79; r = 0.77, respectively) were attained. The compounds that displayed significant (P<. 0.05) correlations with the antioxidant activity were gallic acid, catechin, epicatechin, procyanidin B2, quercetrin, and caffeine. PCA was a suitable approach to check for similarities among tea samples, explaining up to 50% of data variability. Four groups were suggested using HCA, in which cluster 3 showed the highest content of total phenolic compounds, flavonoids, antioxidant activity, gallic acid, and caffeine. All samples included in this group were from C. sinensis. The overall classification capacity obtained by LDA was 82.00%, in which 100% of samples from I. paraguariensis, C. citratus, M. recutita, and P. boldus were adroitly classified, while 60% of teas from P. anisum, 80% of M. piperita teas, and 88.24% of C. sinensis teas were correctly classified.

Original languageEnglish
Pages (from-to)246-254
Number of pages9
JournalFood Research International
Volume60
DOIs
Publication statusPublished - Jun 2014
Externally publishedYes

Keywords

  • Cluster analysis
  • Discriminant analysis
  • Flavonoids
  • Multivariate statistics
  • Quality control

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