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

Abstract

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.