RESUMO
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.
Assuntos
Baccharis/química , Baccharis/classificação , Classificação/métodos , Espectrofotometria Ultravioleta/métodos , Extratos Vegetais/química , Especificidade da EspécieRESUMO
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.