Peanuts functional properties

Attention has been directed toward modifying functional properties of peanut proteins by chemical, enzymatic, and physical approaches. Chemical modification has included acetylation and succinylation treatments (28, 29). Marked improvement in emulsion capacity occurred as a result of this treatment if the proteins were extracted in acid 28). Beuchat et al. 

McWatters and Holmes ( ) developed multiple regression models of the effects of pH and salt concentration on functional properties of soy flour. Design of the experiment and selection of the factors to be Included were based. In part, on earlier findings that emulsion capacity of defatted peanut meal was Inhibited around the Isoelectric point (ca pH 4.0)... 

Lin et al. (15) showed that sunflower meal was superior to soybean and sunflower concentrates or isolates In emulsion capacity. McWatters and Cherry (9) compared select functional properties of defatted soybean, peanut, field pea, and pecan flours and showed that major seed storage proteins were important in emulsifying and foaming properties. Protein solubility was related to the quality of the emulsions and foams. Behavioral characteristics contributed by nonprotein components that occur naturally In the seeds, especially carbohydrates, were Implicated. 

Perkins, D., and Toledo, R. T. (1982). Effect of heat treatment for trypsin inhibitor inactivation on physical and functional properties of peanut protein. J. Food Sci. 47, 917-923. 

Yu, J., Ahmedna, M., et al., 2007. Peanut protein concentrate production and functional properties as affected by processing. Food Chemistry 103 (1), 121—129. 

Multiple regression analysis is a useful statistical tool for the prediction of the effect of pH, suspension percentage, and composition of soluble and insoluble fractions of oilseed vegetable protein products on foam properties. Similar studies were completed with emulsion properties of cottonseed and peanut seed protein products (23, 24, 29, 30, 31). As observed with the emulsion statistical studies, these regression equations are not optimal, and predicted values outside the range of the experimental data should be used only with caution. Extension of these studies to include nonlinear (curvilinear) multiple regression equations have proven useful in studies on the functionality of peanut seed products (33). 

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