Investigation on the homogeneity and stability of aqueous nano cellulose suspensions prepared using pluronic F-127

This paper presents the results of experimental investigation on the aqueous dispersion behaviour of micro crystalline cellulose (MCC) prepared using Pluronic F-127. For this purpose, different concentrations (0.5-3.0 wt.%) of MCC were dispersed in water with the help of ultrasonication technique using various concentrations of Pluronic F-127. The homogeneity of the suspensions and agglomerations were characterized by optical and transmission electron microscopy and the concentration of well dispersed MCC was measured using UV-Vis spectroscopy. Also, the suspensions were subjected to high speed ultracentrifugation at 3000 rpm and observed visually for sedimentation and subsequently, concentration was calculated using UV-Vis, in order to assess the long term stability of the suspensions. Based on these experiments, optimum concentration of Pluronic to disperse different MCC concentrations has been suggested.

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.