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首页> 外文期刊>Beni-Suef University Journal of Basic and Applied Sciences >Plackett-Burman design for screening of process components and their effects on production of lactase by newly isolated Bacillus sp. VUVD101 strain from Dairy effluent
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Plackett-Burman design for screening of process components and their effects on production of lactase by newly isolated Bacillus sp. VUVD101 strain from Dairy effluent

机译:Plackett-Burman设计用于筛选新分离的芽孢杆菌 sp的过程成分及其对乳糖酶产生的影响。乳制品废水中的VUVD101菌株

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In the present investigation, a low-cost fermentation medium was designed to achieve the maximum production of lactase from theBacillussp. VUVD101 strain through the screening of different nutritional and physical variables, using the Plackett-Burman design. Fourteen variables of the fermentation process were selected: incubation time, temperature, pH, RPM, DO, inoculum size, inoculum age, MgSO4,l-Cysteine, KH2PO4, CaCl2, K2PHO4, corn steep liquor and lactose. The selected variables were evaluated through statistical analysis, based on their significance, coefficient value and standard effect plot. The results suggested that six variables, namely, corn steep liquor, lactose, MgSO4, temperature, pH and RPM, had influence with high confidence levels, while the remaining eight variables did not show a significant effect on production. The analysis of the variance value R2(0.96) also showed the model used for prediction to be significant (p?less than?0.05). The plot for the standard effect for each component and its traits provided accurate data by which to select well-suited variables for further optimization. In comparison with the basal medium, 68% higher enzyme activity was achieved from the model of the optimized medium, and lactase activity was found to be 18.31?U/ml.
机译:在本研究中,设计了一种低成本的发酵培养基以从芽孢杆菌中获得最大的乳糖酶产量。通过使用Plackett-Burman设计,通过筛选不同的营养和物理变量来筛选VUVD101菌株。选择发酵过程的十四个变量:孵育时间,温度,pH,RPM,DO,接种量,接种物年龄,MgSO4、1-半胱氨酸,KH2PO4,CaCl2,K2PHO4,玉米浆和乳糖。根据变量的重要性,系数值和标准效果图,通过统计分析对所选变量进行评估。结果表明,玉米浆,乳糖,MgSO4,温度,pH和RPM等六个变量具有高置信度的影响,而其余八个变量未对产量产生显着影响。方差值R2(0.96)的分析还表明,用于预测的模型是显着的(p≤0.05)。每个成分及其特质的标准效果图提供了准确的数据,可通过这些数据选择合适的变量进行进一步优化。与基础培养基相比,从优化培养基的模型中获得了68%的酶活性,而乳糖酶活性为18.31?U / ml。

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