Influence of a cationic polysaccharide on starch functionality.

Fundamental rheology, differential scanning calorimetry and infrared spectroscopy have been used to evaluate the effect of a cationic polysaccharide, chitosan, on the gelatinization, gel formation and retrogradation of maize starch samples, under acidic aqueous conditions. Moderate acidic conditions (0.1molL(-1) acetic acid) have shown a (slight) positive effect on starch gelatinization process and structure development. The presence of chitosan increased the DSC onset gelatinization temperature and also shifted the onset of the storage modulus increase to higher temperatures. Formation of the starch gel, mainly gelation of the leached-out amylose, is somehow hindered by the presence of the cationic polysaccharide and, therefore, the retrogradation of starch at very early stage can be delayed by addition of chitosan. However, long-term retrogradation was slightly increased. FTIR pectroscopy did not reveal any significant interaction between both polysaccharides what is in accordance with the observed rheological behavior. Small additions of chitosan to starch-rich systems may be a useful strategy to obtain new textures with novel phase transition behaviors.

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.