An ultrasound-assisted process is proposed to obtain protein isolates from canola and soybean byproducts. A laboratory-scale probe system (24 kHz, 400W) was used for protein extraction and six levels of energy density were analyzed. Different mixtures of solvents (water, ethanol, methanol, acetone) and temperatures were tested for removing phenolic compounds, and the best results were obtained with ethanol-water (8:2 v/v) and energy density of 104 kJ/L, which minimize protein losses at that step. After extraction of phenolic compounds, samples were alkalinized (pH 12 or 9.5), subjected to ultrasound treatment and the protein precipitated at the isoelectric pH. The highest yield on protein extraction was obtained with 1:20 meal: NaOH solution ratio, applying 150 kJ/L at constant temperature (30 °C), increasing the protein recovery, 72.5% for canola meal and 37.5% for soybean meal, compared to the conventional method. The solubility of both isolates obtained with ultrasound improved ~ 50% at a pH close to neutral and basic, with an increase in free sulfhydryl groups, without significant effects of ultrasound treatment on the subunit fractions of the proteins. These results provide alternatives to develop protein isolates from an undervalued by-product, with better techno-functional properties that can be employed in the food industry.

Wine is a temperature, light, and oxygen-sensitive product, so its physicochemical characteristics can be modified by variations in temperature and time when samples are either sampled, transported, and/or analyzed. These changes can alter its metabolomic fingerprinting, impacting further classification tasks and quality/quantitative analyses. For these reasons, the aim of this work is to compare and analyze the information obtained by different chemometric methods used in a complementary form (PCA, ASCA, and PARAFAC) to study ¹H-NMR spectra variations of four red wine samples kept at different temperatures and time lapses. In conjunction, distinctive changes in the spectra are satisfactorily tracked with each chemometric method. The chemometric analyses reveal variations related to the wine sample, temperature, and time, as well as the interactions among these factors. Moreover, the magnitude and statistical significance of the effects are satisfactorily accounted for by ASCA, while the time-related effects variations are encountered by PARAFAC modeling. Acetaldehyde, formic acid, polyphenols, carbohydrates, lactic acid, ethyl lactate, methanol, choline, succinic acid, proline, acetoin, acetic acid, 1,3-propanediol, isopentanol, and some amino acids are identified as some of the metabolites which present the most important variations.