Ultrasound viscous reduction effects on the proteolysis of soy protein isolate at a limited degree of hydrolysis: Changes in the functional characteristics and protein structure.

High-concentration soy protein isolate was subjected to ultrasonication for viscosity reduction to assist the process of limited enzymatic hydrolysis. Ultrasonication (20 kHz, 10 min, 160 W/L) effectively reduced the viscosity of soy protein isolate at a comparatively high concentration of 14 % (w/v) and promoted the limited enzymatic hydrolysis (controlled degree of hydrolysis of 12 %) with a higher peptide yield than that of the conventional method. The correlations between substrate viscosity and peptide yield, as well as the viscosities of the resulting hydrolysates, were studied. The findings revealed positive correlations between the viscosities of the substrate and hydrolysate, underscoring the potential impact of altering substrate viscosity on the final product. Furthermore, the utilization of ultrasonic viscosity reduction-assisted proteolysis has shown its capability to improve the functional and physicochemical properties, as well as the protein structure of the hydrolysate, while maintaining the same level of hydrolysis. It is worth noting that there were significant alterations in particle size (decrease), ß-sheet content (increase), ß-turn content (increase), and random coil content (increase). Interestingly, ultrasonication unexpectedly impeded the degradation of molecular mass in proteins during proteolysis, while increasing the hydrophobic properties of the hydrolysate. These findings aligned with the observed reduction in bitterness and improvement in emulsifying properties and water-holding capacity.

Comparison of prediction models for soy protein isolate hydrolysates bitterness built using sensory, spectrofluorometric and chromatographic data from varying enzymes and degree of hydrolysis.

The bitterness of soy protein isolate hydrolysates prepared using five proteases at varying degree of hydrolysis (DH) and its relation to physicochemical properties, i.e., surface hydrophobicity (H0), relative hydrophobicity (RH), and molecular weight (MW), were studied and developed for predictive modelling using machine learning. Bitter scores were collected from sensory analysis and assigned as the target, while the physicochemical properties were assigned as the features. The modelling involved data pre-processing with local outlier factor; model development with support vector machine, linear regression, adaptive boosting, and K-nearest neighbors algorithms; and performance evaluation by 10-fold stratified cross-validation. The results indicated that alcalase hydrolysates were the most bitter, followed by protamex, flavorzyme, papain, and bromelain. Distinctive correlation results were found among the physicochemical properties, influenced by the disparity of each protease. Among the features, the combination of RH-MW fitted various classification models and resulted in the best prediction performance.