A 1H-NMR-based metabolomic analysis of propolis from Santa Catarina state

16th IUFoST World Congress of Food Science and Technology: Addressing Global Food Security and Wellness through Food Science and TechnologyPropolis is a resinous biomass produced by honeybees from exudates of local flora. It has been used since ancient times in folk medicine and in recent years has been added to foods and beverages to improve health and prevent diseases. The chemical composition of propolis is highly variable and depends on the climate, season, specie of bee, and mainly the local flora visited by bees to collect resin. In order to identify groups of chemical similarity among samples (n=20 autumn, n=16 winter, n=19 spring, n=17 summer) of propolis produced in Santa Catarina (SC) state (southern Brazil - 2010), lyophilized ethanolic extracts (200 mg/ml, EtOH 70%, v/v) were solubilized in MeOD3 (700l) and analyzed by NMR spectroscopy. One-dimensional 1HNMR spectra were acquired at a magnetic field strength of 500,13/125,03 MHz using a Varian Inova 500 MHz equipment and standard conditions of data acquisition. The 1H-NMR peak list data set was processed under MetaboAnalyst 2.0. suite, computing the resonances at 0.80- 12ppm spectral window. Principal Components Analysis (PCA) score scatter plots (PC1 88.2% x PC2 2.2%) clearly demonstrated samples discriminated mainly according to the season of production. These results suggest that not only geographical origin is important for the classification of propolis, but the seasonal effects as well. Since seasons directly influence the flora available from where bees collect resin, the propolis chemical profile can be significantly modified over the seasons even from a same geographical origin.info:eu-repo/semantics/publishedVersio

A machine learning and chemometrics assisted interpretation of spectroscopic data: a NMR-based metabolomics platform for the assessment of Brazilian propolis

In this work, a metabolomics dataset from 1H nuclear magnetic resonance spectroscopy of Brazilian propolis was analyzed using machine learning algorithms, including feature selection and classification methods. Partial least square-discriminant analysis (PLS-DA), random forest (RF), and wrapper methods combining decision trees and rules with evolutionary algorithms (EA) showed to be complementary approaches, allowing to obtain relevant information as to the importance of a given set of features, mostly related to the structural fingerprint of aliphatic and aromatic compounds typically found in propolis, e.g., fatty acids and phenolic compounds. The feature selection and decision tree-based algorithms used appear to be suitable tools for building classification models for the Brazilian propolis metabolomics regarding its geographic origin, with consistency, high accuracy, and avoiding redundant information as to the metabolic signature of relevant compounds.The work is partially funded by ERDF -European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEstOE/ EEI/UI0752/2011. RC's work is funded by a PhD grant from the Portuguese FCT ( ref. SFRH/BD/66201/2009)

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

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.Financial support for this investigation by National Council for Scientific and Technological Development (CNPq), Coordination for the Improvement of Higher Education Personnel (CAPES), Brazilian Biosciences National Laboratory (LNBioCNPEM/MCTI), Foundation for Support of Scientific and Technological Research in the State of Santa Catarina (FAPESC), and Portuguese Foundation for Science and Technology (FCT) is acknowledged. The research fellowship granted by CNPq to the first author is also acknowledged. The work was partially funded by a CNPq and FCT agreement through the PropMine grant