响应表面方法和基于人工神经网络的媒体优化,用于Aureobasidium pullulansans中的pullulan生产
Nageswar Sahu1, Biswanath Mahanty1, Dibyajyoti Haldar1
1Division of Biotechnology, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamil Nadu, India.
International journal of biological macromolecules
|November 25, 2024
概括
优化碳和来源显著提高了Aureobasidium pullulans的普卢兰产量. 一个人工神经网络模型准确地预测了高产量,经过实验结果的验证.
科学领域:
- 生物技术是生物技术.
- 微生物发酵 微生物发酵
- 生物聚合物生产 生物聚合物生产
背景情况:
- 普鲁兰是一种微生物多糖,具有多种工业应用.
- 优化发酵条件对于高效的pullulan产量至关重要.
- 普鲁兰的Aureobasidium是用于普鲁兰生物合成的关键微生物.
研究的目的:
- 选和优化碳和的来源,以提高pullulan的生产.
- 通过实验设计,识别影响普鲁兰产量的重要因素.
- 开发和验证Pullulan发酵的预测模型.
主要方法:
- 选各种碳 (糖,果糖,葡萄糖) 和来源 ((NH4) 2SO4,尿素,NaNO3).
- 普莱克特-伯曼和中央复合材料设计用于因子识别和优化.
- 开发一个超参数优化的人工神经网络 (ANN) 模型用于预测.
主要成果:
- 糖和NaNO3被确定为最佳的碳和来源,产生9.33g L-1 pullulan.
- KH2PO4,NaCl和糖是Plackett-Burman设计发现的重要因素.
- 该ANN模型实现了高预测精度 (R2:0.96) 并预测了31.9g L-1的产量,与观察到的30.17g L-1非常接近.
结论:
- 使用糖和NaNO3的优化发酵条件显著提高了pullulan的生产.
- 人工神经网络模型提供了一种可靠和准确的方法来预测pullulan产量.
- 这项研究表明了优化微生物多糖发酵的强有力的方法.
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