Vegetable proteins, compositional analysi

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). 

Compared to refined vegetable oils, the compositions of crude vegetable oils and oil and fat products are more complicated. These samples contain proteins, carbohydrates, and minerals that interfere with HPLC separation and reduce the lifetime of the HPLC column. These compounds need to be largely eliminated from the extract before HPLC analysis. Saponification and heating are used to weaken sample matrices to allow the solvent to fully access all tocopherols and tocotrienols of the sample. Liquid/liquid extraction is used to remove these polar compounds from the organic solvent layer that contains tocopherols and tocotrienols. The normal-phase HPLC method is usually used for crude vegetable oils and vegetable oil products reversed-phase HPLC can be used for animal fat products. 

---