ARS | Publication request: Fuzzy Clustering-Based Modeling of Surface Interactions and Emulsions of Selected Whey Protein Concentrate Combined to i-Carrageenan and Gum Arabic Solutions

Submitted to: Journal of Food Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 30, 2008
Publication Date: August 12, 2008
Citation: Samhouri, M., Abu-Ghoush, M., Yaseen, E., Herald, T.J. 2008. Fuzzy Clustering-Based Modeling of Surface Interactions and Emulsions of Selected Whey Protein Concentrate Combined to i-Carrageenan and Gum Arabic Solutions. Journal of Food Engineering. 91:10-17.

Interpretive Summary: The food industry and academia have expressed an interest in gum–protein mixtures because of their contributions to stability and functionality. The prediction of surface properties of whey protein concentrate (WPC) in a combination with i-carrageenan or gum Arabic is considered as a complex system, so using the conventional technology to model these properties results in significant discrepancies between simulation results and experimental data. Fuzzy logic and fuzzy inference system (FIS) is an effective technique for the identification and modeling of complex nonlinear systems. Fuzzy logic is particularly attractive due to its ability to solve problems in the absence of accurate mathematical models. Thus, this complex nonlinear system fits within the realm of fuzzy logic technique. The application of fuzzy modeling achieved high accuracies and proved to be a reliable predictive model for this gum-protein system.

Technical Abstract: Gums and proteins are valuable ingredients with a wide spectrum of applications. Surface properties (surface tension, interfacial tension, emulsion activity index “EAI” and emulsion stability index “ESI”) of 4% whey protein concentrate (WPC) in a combination with '- carrageenan (0.05%, 0.1%, and 0.5%) or gum Arabic (0.5%, 1% and 5%) were investigated. The results showed that the addition of '-carrageenan to 4% WPC significantly increased the interfacial tension, EAI and ESI. In addition, a fuzzy-based clustering model was used to predict the surface properties. The fuzzy model achieved accuracies of (94%, 97%, 98% and 94%) for predicting (EAI, ESI, surface tension, and interfacial tension), respectively.