Optimization of culture parameters for extracellular protease production from a newly isolated Pseudomonas sp using response surface and artificial neural network models
Optimization of culture parameters for extracellular protease production from a newly isolated Pseudomonas sp using response surface and artificial neural network models
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DOI：
10.1016/j.procbio.2003.11.009
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发表时间：
2004-10-29
影响因子：
4.4
通讯作者：
Banerjee, R
中科院分区：
文献类型：
--
作者：
Dutta, JR;Dutta, PK;Banerjee, R
采用径向基函数（RBF）、人工神经网络（ANN）和响应面法（RSM）建立自变量组合效应的预测模型根据响应面模型和人工神经网络模型的二次型得到的最佳操作条件为pH7.6，温度38 ℃，接种体积为1.5，孵育24小时内预测蛋白酶活性为58.5 U/ml。从ANN和RSM模型得到的归一化百分比均方误差分别为0.05和0.1%。结果表明，人工神经网络的预测精度比响应面更高。这种优越性的人工神经网络比其他多因素的方法，可以使这种估计技术的发酵监测和控制的一个非常有用的工具。(C)2003爱思唯尔有限公司。保留所有权利。
Radial basis function (RBF) artificial neural network (ANN) and response surface methodology (RSM) were used to build a predictive model of the combined effects of independent variables (pH, temperature, inoculum volume) for extracellular protease production from a newly isolated Pseudomonas sp. The optimum operating conditions obtained from the quadratic form of the RSM and ANN models were pH 7.6, temperature 38 degreesC, and inoculum volume of 1.5 with 58.5 U/ml of predicted protease activity within 24 h of incubation. The normalized percentage mean squared error obtained from ANN and RSM models were 0.05 and 0.1%, respectively. The results demonstrated an higher prediction accuracy of ANN compared to RSM. This superiority of ANN over other multi factorial approaches could make this estimation technique a very helpful tool for fermentation monitoring and control. (C) 2003 Elsevier Ltd. All rights reserved.