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Optimization of culture parameters for extracellular protease production from a newly isolated Pseudomonas sp. using response surface and artificial neural network models

Paper ID Volume ID Publish Year Pages File Format Full-Text
36760 45146 2004 6 PDF Available
Title
Optimization of culture parameters for extracellular protease production from a newly isolated Pseudomonas sp. using response surface and artificial neural network models
Abstract

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 °C, 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.

Keywords
Optimization; Extracellular protease; Pseudomonas sp.; Response surface methodology; Central composite design; Artificial neural network; Radial basis function
First Page Preview
Optimization of culture parameters for extracellular protease production from a newly isolated Pseudomonas sp. using response surface and artificial neural network models
Publisher
Database: Elsevier - ScienceDirect
Journal: Process Biochemistry - Volume 39, Issue 12, 29 October 2004, Pages 2193–2198
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering