Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis 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.

Polyphasic approach including MALDI-TOF MS/MS analysis for identification and characterisation of Fusarium verticillioides in Brazilian corn kernels

Fusarium verticillioides is considered one of the most important global sources of fumonisin contamination in food and feed. Corn is one of the main commodities produced in the Northeastern Region of Brazil. The present study investigated potential mycotoxigenic fungal strains belonging to the F. verticillioides species isolated from corn kernels in 3 different Regions of the Brazilian State of Pernambuco. A polyphasic approach including classical taxonomy, molecular biology, MALDI-TOF MS and MALDI-TOF MS/MS for the identification and characterisation of the F. verticillioides strains was used. Sixty F. verticillioides strains were isolated and successfully identified by classical morphology, proteomic profiles of MALDI-TOF MS, and by molecular biology using the species-specific primers VERT-1 and VERT-2. FUM1 gene was further detected for all the 60 F. verticillioides by using the primers VERTF-1 and VERTF-2 and through the amplification profiles of the ISSR regions using the primers (GTG)5 and (GACA)4. Results obtained from molecular analysis shown a low genetic variability among these isolates from the different geographical regions. All of the 60 F. verticillioides isolates assessed by MALDI-TOF MS/MS presented ion peaks with the molecular mass of the fumonisin B1 (721.83 g/mol) and B2 (705.83 g/mol)