Tea is the second most consumed beverage aside from water, and has gained much attention due to its health-promoting benefits. Among the benefit effects include antimutagenic, anticancer and anti apoptotic, neuroprotective, hypoglycemic and antihyperglycemic, antioxidant, antimicrobial, and anti inflammatory. These biological activities are associated in part to the antioxidant activity of chemical compounds present in teas, especially flavonoids and phenolic compounds (Chapter 1). The aim of this study was to evaluate the phenolic
compounds and in vitro antioxidant activity of teas consumed in Brazil. In the Chapter 2, 51 Brazilian teas of eight different species (Camellia sinensis, Peumus boldus, Matricaria recutita, Baccharis trimera, Cymbopogon citratus, Pimpinella anisum, Mentha piperita and Ilex paraguariensis) were analyzed in terms of the phenolic compounds, color, and in vitro antioxidant activity using FRAP and DPPH. The dataset was analyzed using PCA, HCA, and LDA. Gallic acid, catechin, epicatechin, procyanidin B2, quercetrin, and caffeine displayed a significant correlation (p < 0.05) with antioxidant activity. Using the PCA was possible to have a suitable approach to check the similarities among tea samples, explaining up to 50% of data variability and four clusters were suggested by HCA. The overall classification of 82% was obtained by LDA, 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, and 88% of C. sinensis teas were correctly classified. Chapter 3 was modelled the extraction of phenolic compounds and in vitro antioxidant activity from mixtures of green, white, and black teas (Camellia sinensis) using a simplex-centroid design couple multiple regression analysis. All proposed models were significant (p < 0.05) and showed high determination coefficients (R2adj > 0.80). A simultaneous optimization was performed using the desirability function and optimum point to maximize the extraction of epicatechin, epigallocatechin gallate, epicatechin gallate, as well as the antioxidant activity (DPPH and FRAP) was suggested. Therefore, in the Chapter 3 the results showed up that white tea was the best solution for obtaining the higher content of antioxidant compounds, then the Chapter 4 was destined to optimize the extraction of antioxidant compounds from white tea by BoxBehnken design and the compounds were identified by LC-DAD-MS/MS. All mathematical
models proposed were able to explain up more that 85% of data variation and the optimization performed using DPPH, ABTS, FRAP, (-)-epigallocatechin gallate, and (-)-epicatechin gallate suggested the time of 10 min, temperature of 66 °C and the 30% ethanol solution as optimum point. The principal compounds identified in the optimum point by mass spectrometry were gallic acid, 5-galloylquinic acid, caffeine, theobromine, gallocatechin, epigallocatechin, epicatechin, epigallocatechin gallate, and epicatechin gallate. Nevertheless, this study showed that different analytical determination associated chemometrics tools can be used to explore and classify the samples, thus it can be used for extraction processes of bioactive compounds
of plant samples.