Study on the extraction optimization of rice bran protein based on GRNN

According to the general problems including the low yield and the deep colour of product during the alkali-extraction process of rice bran protein, the cross-action of different factors was discussed through central composite experiments on the basis of the key factors determined by single factor experiments.Furthermore, the optimization of interaction-effect including protein yield and color were performed by generalized regression neural network (GRNN) method.The GRNN model results showed that 61.0% protein yield and 53.3 color value were obtained under the conditions of temperature 36.5℃, ratio of solve to material 11.5:1 (v/w) , pH value 10.9, and auxiliary agent amount 0.56%, which existed a deviation of 3.6% and 4.7% with actual values, respectively.Compared with the conditions for industrial production, the protein extraction yield and colour value raised 31.4% and 43.3%, respectively, under the optimized conditions.It was proved that the optimized extraction conditions of rice bran protein by GRNN had great pragmatic value for industrial production.