Preparation of oil-in-water emulsions stabilized by faba bean protein-grape leaf polyphenol conjugates: pH-, salt-, heat-, and freeze-thaw-stability.

BACKGROUND: Plant proteins are being increasingly utilized as functional ingredients in foods because of their potential health, sustainability, and environmental benefits. However, their functionality is often worse than the synthetic or animal-derived ingredients they are meant to replace. The functional performance of plant proteins can be improved by conjugating them with polyphenols. In this study, the formation and stability of oil-in-water emulsions prepared using faba bean protein-grape leaf polyphenol (FP-GLP) conjugates as emulsifiers. Initially, FP-GLP conjugates were formed using an ultrasound-assisted alkali treatment. Then, corn oil-in-water emulsions were prepared using high-intensity sonication (60% amplitude, 10 min) and the impacts of conjugate concentration, pH, ionic strength, freezing-thawing, and heating on their physicochemical properties and stability were determined. RESULTS: Microscopy and light scattering analysis showed that oil-in-water emulsions containing small oil droplets could be formed at conjugate concentrations of 2% and higher. The addition of salt reduced the electrostatic repulsion between the droplets, which increased their susceptibility to aggregation. Indeed, appreciable droplet aggregation was observed at ≥ 50 mmol/L sodium chloride. The freeze-thaw stability of emulsions prepared with protein-polyphenol conjugates was better than those prepared using the proteins alone. In addition, the emulsions stabilized by the conjugates had a higher viscosity than those prepared by proteins alone. CONCLUSION: This study showed that FP-GLP conjugates are effective plant-based emulsifiers for forming and stabilizing oil-in-water emulsions. Indeed, emulsions formed using these conjugates showed improved resistance to pH changes, heating, freezing, and salt addition. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Machine learning approach for predicting the antifungal effect of gilaburu (Viburnum opulus) fruit extracts on Fusarium spp. isolated from diseased potato tubers.

This work addresses the mathematical model building to detect the diameter of the inhibition zone of gilaburu (Viburnum opulus L.) extract against eight different Fusarium strains isolated from diseased potato tubers. Gilaburu extracts were obtained with acetone, ethanol or methanol. The isolated Fusarium strains were: F. solani, F. oxysporum, F. sambucinum, F. graminearum, F. coeruleum, F. sulphureum, F. auneaceum and F. culmorum. In general, it was observed that ethanolic extracts showed highest antifungal activity. The antifungal activity of extracts was evaluated with machine learning (ML) methods. Several ML methods (classification and regression trees (CART), support vector machines (SVM), k-Nearest Neighbors (k-NN), artificial neural network (ANN), ensemble algorithms (EA), AdaBoost (AB) algorithm, gradient boosting (GBM) algorithm, random forests (RF) bagging algorithm and extra trees (ET)) were applied and compared for modeling fungal growth. From this research, it is clear that ML methods have the lowest error level. As a result, ML methods are reliable, fast, and cheap tools for predicting the antifungal activity of gilaburu extracts. These encouraging results will attract more research efforts to implement ML into the field of food microbiology instead of traditional methods.