基于BP神经网络的牡蛎抗氧化活性肽制备工艺优化
Optimization of enzymatic processing for antioxidant peptides from oyster based on BP neural network
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摘要: 以牡蛎为原料,通过响应面和BP神经网络模型对牡蛎蛋白酶解过程、工艺条件进行优化研究,实现酶解过程中可控制备牡蛎抗氧化活性肽。结果表明:采用胰酶水解牡蛎,经响应面优化得到最佳酶解条件为:加酶量0.28%,温度55℃,时间3.4 h,此条件下酶解产物·OH清除率可达到78.4%。在此基础上,以酶解过程中的响应因子-游离谷氨酸含量来监控牡蛎蛋白水解过程,运用BP神经网络数学模型拟合蛋白水解过程与产物活性之间的关系。构建出以加酶量、料水比和酶解体系游离谷氨酸浓度为神经网络输入,酶解产物·OH清除率为输出的BP神经网络模型,以实现在预期时间内通过实时监测游离谷氨酸含量即可得出产物活性变化情况,并定点终止反应的目的。模型预测值与实验值相关性系数r为0.9943,平均相对误差值为2.1%。该模型拟合性能良好,与牡蛎蛋白酶解过程具有高度相关性,可应用于牡蛎抗氧化活性肽的在线监控与可控制备。Abstract: In order to achieve the controllable preparation of antioxidant peptides from oyster,enzymatic hydrolysis conditions were optimized on the basis of the response surface methodology( RSM) and neural network models.In this study,pancreatin was used for the oyster hydrolysis. The results of RSM showed that the optimal hydrolysis condition was 0.28% pancreatin dosage,temperature of 55 ℃,and hydrolysis duration of 3.4 h,under this condition the ·OH clearance rate of its enzymatic prodcut reached 78.4%.The free glutamic acid concentration was used as the monitor of enzymatic hydrolysis process.The back propagation( BP) neural network model was applied to fit for the relationship between enzymatic hydrolysis process and activity of the hydrolysate. The correlation coefficient between predict value and confirmatory experiment were 0.9943,and the average relative error was 2.1%,which indicated that this BP neural network model showed good performance. It was an effective way to monitor the enzymatic hydrolysis on- line,and made it possible to realize controllable preparation of antioxidant peptides.