Optimization of the Nutritional Parameters for Enhanced Production of B. subtilis SPB1 Biosurfactant in Submerged Culture Using Response Surface Methodology.

Ines Mnif, Semia Chaabouni-Ellouze, Dhouha Ghribi
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引用次数: 45

Abstract

Nutritional requirements can contribute considerably to the production cost and the bioprocess economics. Media optimisation using response surface methodology is one of the used methods to ameliorate the bioprocess economics. In the present study, biosurfactant production by Bacillus subtilis SPB1 was effectively enhanced by response surface methodology. A Plackett-Burman-based statistical screening procedure was adopted to determine the most important factor affecting lipopeptide production. Eleven variables are screened and results show that glucose, K(2)HPO(4), and urea concentrations influence the most biosurfactant production. A Central Composite Design was conducted to optimize the three selected factors. Statistical analyses of the data of model fitting were done by using NemrodW. Results show a maximum predicted biosurfactant concentration of 2.93 (±0.32) g/L when using 15 g/L glucose, 6 g/L urea, and 1 g/L K(2)HPO(4). The predicted value is approximately 1.65 much higher than the original production determined by the conventional one-factor-at-a-time optimization method.

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响应面法优化枯草芽孢杆菌SPB1生物表面活性剂深层培养营养参数
营养需求对生产成本和生物过程经济有很大影响。利用响应面法优化培养基是改善生物过程经济的常用方法之一。在本研究中,响应面法有效地提高了枯草芽孢杆菌SPB1的生物表面活性剂产量。采用基于plackett - human的统计筛选程序来确定影响脂肽产生的最重要因素。筛选了11个变量,结果表明葡萄糖、K(2)、HPO(4)和尿素浓度对生物表面活性剂的产量影响最大。采用中心复合设计对所选因子进行优化。采用NemrodW软件对拟合数据进行统计分析。结果表明,当使用15 g/L葡萄糖、6 g/L尿素和1 g/L K(2)HPO(4)时,生物表面活性剂的最大预测浓度为2.93(±0.32)g/L。预测值约为1.65,远高于常规单因素优化方法确定的原始产量。
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