Antioxidant properties of phenolic compounds from Baccharis articulata and B. usterii

As a continuation of our research on the Baccharis genus, the evaluation of the antioxidant effect by TRAP and TBARS assays of three isolated compounds from n-butanol fractions of B. articulata and B. usterii is reported. The structures of these compounds were established as 4-O-beta-D-glucopyranosyl-3,5-dimethoxybenzyl-methanol (1), 5-O-[E]-dicaffeoylquinic acid (2), and 7-hydroxy-5,4'-dimethoxyflavone (3). In the TRAP assay it was possible to observe an antioxidant effect of both n-butanol fractions at 1.25 microg/mL. Among the isolated compounds, compound 2 displayed a remarkable contribution to the total antioxidant capacity of the n-butanol fraction of B. usterii. Moreover, the n-butanol fractions of both species, compounds 1 and 2 at 40.0 microg/mL were efficient in protection of lipid peroxidation in the TBARS experiment. They are promising lead compounds for use in medicinal chemistry studies.

Analysis of propolis from Baccharis dracunculifolia DC. (Compositae) and its effects on mouse fibroblasts

This paper confirms Baccharis dracunculifolia DC. (Compositae) as the main botanical source of the propolis from southeastern Brazil (state of São Paulo) investigated to ascertain specific biological activity in relation to mouse NIH-3T3 fibroblasts, skin cells directly involved in the cicatrization processes. Flavonoid and total phenolic compounds were determined by spectrophotometry, and chemical composition by HPLC; the chromatographic profile, characterized largely by flavonoids and aromatic acids, was found to be qualitatively similar to that of Baccharis dracunculifolia DC. The adsorption of phenolic compounds in the propolis to skin powder was also investigated, and 68% of these compounds adsorbed to the skin powder. At concentrations from 0.12 to 7.81 microg/ml, the propolis revealed no statistical significant differences from its control solutions; however, at concentrations of 31.25 microg/ml or more, the propolis was toxic to NIH-3T3 cells. Thus, the propolis from Baccharis dracunculifolia DC. (Compositae) presents an in vitro concentration-dependent toxicity on mouse NIH-3T3 fibroblasts.

Numerical Taxonomy Characterization of Baccharis Genus Species by Ultraviolet-Visible Spectrophotometry

Numerical taxonomy characterization of Baccharis genus species was performed using ultraviolet-visible spectrophotometry. The aim was to present a more convenient, more practical, more economic and faster method based on chemometric methods and UV-vis absorbance to give the most information about species identity and discrimination, especially when their classification has been doubtful. Three Baccharis species: B. genistelloides Persoon var. trimera (Less.) DC, B. milleflora (Less.) DC, and B. articulata (Lam.) Persoon were included in the study. With the help of principal component analysis (PCA) and cluster analysis (CA), we could characterize the three species. Application of soft independent modeling of class analogy (SIMCA) and K-nearest neighbor (KNN) methods on a training set of 65 extracts resulted in models that correctly classified all samples of an independent validation set, eight samples of B. genistelloides Persoon var. trimera (Less.) DC and one sample donated by Prof. Alarich Schultz Herbarium, Porto Alegre-RS, Brazil.

Differentiation of species of the Baccharis genus by HPLC and chemometric methods

Phytochemical investigation of the aerial parts of three Baccharis species (Asteraceae family) was performed using HPLC and chemometric methods, with the objective of distinguishing between three morphologically very similar species: Baccharis genistelloides Persoon var. trimera (Less.) DC, B. milleflora (Less.) DC and B. articulata (Lam.) Persoon. With the help of Principal Component Analysis (PCA) and variance weights, it was possible to characterize the chromatographic profiles of the alcoholic extracts of the three species. Application of Soft Independent Modeling of Class Analogy (SIMCA) and K-Nearest Neighbor (KNN) methods on a training set of 74 extracts resulted in models that correctly classified all eight samples in an independent test set.