UV-Visible Scanning Spectrophotometry and Chemometric Analysis as Tools to Build Descriptive and Classification Models for Propolis from Southern Brazil
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Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. Recent studies classified Brazilian propolis into 12 groups based on physiochemical characteristics and different botanical origins. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis’ chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. UV-Visible (UV-Vis) scanning spectrophotometry meets those prerequisites and was adopted, affording a spectral dataset containing the chemical profiles of hydroalcoholic extracts of sixty five propolis samples collected over the distinct seasons of year 2014, in southern Brazil. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA), by using bioinformatics tools supported by scripts written in the R language. The spectrophotometric profile approach associated with chemometric analyses allowed identifying a different pattern in samples of propolis produced during the summer season over the other seasons. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds (λ = 280−350 ηm), suggesting that besides the biological activities presented by those secondary metabolites, they are also relevant for the discrimination and classification of that complex matrix through bioinformatics tools.
KeywordsPropolis UV-Vis scanning spectrophotometry Chemometrics Metabolic profile Botanical source Seasonality
Financial support and the research fellowship to the later author from CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) are acknowledged.
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