基于GRNN的米糠蛋白提取条件优化研究
详细信息Study on the extraction optimization of rice bran protein based on GRNN
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摘要: 针对米糠蛋白碱法生产中存在的产品得率低、色泽深等普遍问题,本文在单因素实验确定的几个蛋白提取关键影响因素基础上,采用中心组合实验的方法探讨了因素之间交互作用的问题,最后利用广义回归神经网络技术(general regression neural network,GRNN)对蛋白提取率和提取液色泽进行了多目标优化问题研究。GRNN模型结果显示:在温度36.5℃,液料比11.5:1(v/w),pH10.9,辅助剂用量0.56%的条件下,米糠蛋白提取率理论值为61.0%,色度值为53.3,与实际值的误差分别为3.6%和4.7%,且相比较目前工业生产普遍使用的生产条件,蛋白提取率与色度值分别提高了31.4%与43.3%,采用GRNN方法优化的米糠蛋白提取条件具有很好的实用价值。Abstract: 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.
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Keywords:
- GRNN;rice bran;protein;extraction;optimization; /
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