发芽糙米酶解提高还原糖产量的响应面优化

Hoang Vo Minh, Toan Nguyen Duc
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引用次数: 0

摘要

利用α-淀粉酶和葡萄糖淀粉酶对胚芽米进行水解,与传统的提取方法相比,可以提高还原糖含量,降低黏度,提高乳液得率。液化实验以底物比α-淀粉酶浓度和α-淀粉酶浓度、不同水解时间为影响因素。采用中心组合设计方法,建立多元模型进行糖化实验。结果表明,在1:2的底物比下,0.5% α-淀粉酶浓度(约为0.5%)。11U/g淀粉)和50分钟水解时间作为下一步实验的基础。回归模型方差分析显示,二次模型显著(p < 0.05),表明该模型适用于所有数据。模型的信度R2 = 0.993,表明所建立的回归模型与数据集的拟合率为99.3%。CV = 1.19%表明所进行的实验具有较好的精密度和可靠性。葡萄糖淀粉酶的最佳水解条件浓度为0.399%(约为0.399%)。在温度59.813℃、水解时间160.468 min条件下,DE含量最高,为25.245%,高于非酶法(DE = 8.985±0.062)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Response surface optimization of enzymatic hydrolysis of germinated brown rice for higher reducing sugar production
The hydrolysis of germ rice by the use of α-amylase and glucoamylase enzymes will help increase the reducing sugar content, reduce viscosity, and improve the yield of milk solution compared to the traditional extraction method. The liquefaction experiment was arranged with two factors, which are substrate ratio: α-amylase concentration and α-amylase concentration: different hydrolysis time. The saccharification experiment was carried out based on a multivariate model according to the Central Composite Design method. As a result, a 1 : 2 substrate ratio, 0.5% α-amylase concentration (approx. 11U/g starch) and 50 minutes hydrolysis time were selected as the basis for the next experiment. Analysis of variance in the regression model showed that the quadratic model was significant (p < 0.0001). Lack of fit (p > 0.05) this indicates that the model is suitable for all data. The reliability of the model R2 = 0.993 shows that the built regression model fits the data set 99.3%. CV = 1.19% indicated a better precision and reliability of the experiments carried out. Optimal conditions for hydrolysis of glucoamylase concentration of 0.399% (approx. 119.863U/g starch), temperature of 59.813°C and hydrolysis time of 160.468 minutes gave the highest DE content at 25.245% and higher than the non-enzymatic method (DE = 8.985 ± 0.062).  
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