Effect of whey protein concentrate on texture of fat-free desserts: sensory and instrumental measurements

It is important to understand how changes in the product formulation can modify its characteristics. Thus, the objective of this study was to investigate the effect of whey protein concentrate (WPC) on the texture of fat-free dairy desserts. The correlation between instrumental and sensory measurements was also investigated. Four formulations were prepared with different WPC concentrations (0, 1.5, 3.0, and 4.5 wt. (%)) and were evaluated using the texture profile analysis (TPA) and rheology. Thickness was evaluated by nine trained panelists. Formulations containing WPC showed higher firmness, elasticity, chewiness, and gumminess and clearly differed from the control as indicated by principal component analysis (PCA). Flow behavior was characterized as time-dependent and pseudoplastic. Formulation with 4.5% WPC at 10 °C showed the highest thixotropic behavior. Experimental data were fitted to Herschel-Bulkley model. The addition of WPC contributed to the texture of the fat-free dairy dessert. The yield stress, apparent viscosity, and perceived thickness in the dairy desserts increased with WPC concentration. The presence of WPC promotes the formation of a stronger gel structure as a result of protein-protein interactions. The correlation between instrumental parameters and thickness provided practical results for food industries.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.