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Freezing can adversely affect the quality of liquid egg through complex inter-particle interactions of egg micro-particles. The effect of pectin, PG-Alginate (PGA), Arabic Gum, maltodextrin and defrost temperature on the rheological properties, microstructure and color of frozen egg was studied. The fresh sample exhibited a light yellow color with high values of L and positive values of a and b. Freezing resulted in a reduced L value with slight changes in a and b. The additives could enhance the lightness with the closest values of L to that of the fresh sample detected for PGA and pectin. Fresh egg with a low viscosity exhibited near-Newtonian flow behavior. However, the freezing-thawing significantly increased the viscosity. Accordingly, the flow behavior index of the frozen samples was decreased significantly along with a sharp increase in the consistency index, revealing a pseudo-plastic behavior. All frozen samples exhibited higher viscosities than the fresh sample. Fresh samples contained evenly distributed micro-particles ranging from 0.05 to 5.50 µm centering at 1.27 µm. Freezing resulted in aggregated particles with significantly larger sizes. Maltodextrin significantly reduced the particle size. Further reduction was achieved by the addition of pectin, PGA, and Arabic gum. Smallest particle size distributions were achieved at pectin and PGA concentrations of 0.25 and 0.1%, respectively. The results of this study can be employed for the development of new products based on frozen egg with no added sugar or salt, while maintaining the physical and functional properties of the final product. [ABSTRACT FROM AUTHOR]

In this work, Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were employed for the data reduction of a numerically simulated extraction process of sugar in an industrial RT2 extractor. The numerical model developed in OpenFOAM library was first validated using actual plant data and its stability and sensitivity to the processing variables was tested. Then, the model was used to generate data of juice and pulp sugar concentrations as affected by the main processing parameters including draft, Silin number, and capacity. The data were modelled using RSM and ANN. Both RSM and ANN were able to predict the data accurately, however, R² values obtained for ANN were slightly higher. Since the numerical model can be time consuming to be solved for all data ranges, the regression equation obtained by the RSM method or the network created according to the ANN model can be utilized as fast and ready to use tools to optimize the extractor. [ABSTRACT FROM AUTHOR]