Utilize este identificador para referenciar este registo: http://hdl.handle.net/1822/38890

TítuloDiscrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data
AutorTomazzoli, Maíra Maciel
Neto, Remi Dal Pai
Moresco, Rodolfo
Westphal, Larissa
Zeggio, Amélia Regina Somensi
Specht, Leandro
Costa, Christopher
Rocha, Miguel
Maraschin, Marcelo
Data21-Out-2015
EditoraInformationsmanagement in der Biotechnologie e.V. (IMBio e.V.)
CitaçãoTomazzoli, Maíra Maciel; Neto, Remi Dal Pai; Moresco, Rodolfo; Westphal, Larissa; Zeggio, Amélia Regina Somensi; Specht, Leandro; Costa, Christopher; Rocha, Miguel; Maraschin, Marcelo, Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data. Journal of Integrative Bioinformatics, 12(4), 279, 2015
ResumoPropolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. 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. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.
Tipoarticle
URIhttp://hdl.handle.net/1822/38890
DOI10.2390/biecoll-jib-2015-279
ISSN1613-4516
e-ISSN1613-4516
Arbitragem científicayes
AcessoopenAccess
Aparece nas coleções:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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