Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/40291

TitleMetabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning
Author(s)Maraschin, Marcelo
Somensi-Zeggio, A.
Oliveira, Simone K.
Kuhnen, S.
Tomazzoli, Maíra Maciel
Raguzzoni, Josiane C.
Zeri, A. C. M.
Carreira, Rafael
Correia, Sara
Costa, Christopher Borges
Rocha, Miguel
Issue date2016
PublisherAmerican Chemical Society
JournalJournal of Natural Products
CitationMaraschin, Marcelo; Somensi-Zeggio, Amélia; Oliveira, Simone K.; Kuhnen, Shirley; Tomazzoli, Maíra M.; Raguzzoni, Josiane C.; Zeri, Ana C. M.; Carreira, R.; Correia, S.; Costa, Christopher; Rocha, Miguel, Metabolic profiling and classification of propolis samples from Southern Brazil: An NMR-based platform coupled with machine learning. Journal of Natural Products, 79(1), 13-23, 2016
Abstract(s)The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.
TypeArticle
URIhttp://hdl.handle.net/1822/40291
DOI10.1021/acs.jnatprod.5b00315
ISSN0163-3864
e-ISSN1520-6025
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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