Optimization of medium constituents for Cephalosporin C production using response surface methodology and artificial neural networks

J Biochem Tech

Volume 1, Issue3, April 2009, Page No. 69-74

Optimization of medium constituents for Cephalosporin C production using response surface methodology and artificial neural networks

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Venkata Ratna Ravi Kumar Dasari, Sri Rami Reddy Donthireddy, Murali Yugandhar Nikku and Hanumantha Rao Garapati

Center for Biotechnology, Department of Chemical Engineering, University College of Engineering, Andhra University, Visakhapatnam-530 003, India

Received: 21 January 2009 / Received in revised form: 14 February 2009, Accepted: 23 February 2009, Published online: 25 February 2009

Abstract

Artificial neural networks (ANN) and response surface methodology
(RSM) were used to build a model to describe the effects of four
independent variables (moisture content, concentrations of glucose,
ammonium nitrate and methionine) on the yield of cephalosporin C
(CPC) from Acremonium chrysogenum under solid state
fermentation. The respective uses of RSM and ANN were found to
be effective in locating the optimum conditions within the range
fixed from the preliminary runs. When compared with the
predictions given by RSM, ANN was found to be superior in
describing the fermentation process for the production of CPC.
When a global optimization routine was employed to optimize the
equation resulted from the neural networks, the optimum predicted
antibiotic yield was found to be 29.4 mg/g which is 14.8 % higher
than the optimum value obtained from preliminary runs, and 9.2 %
higher than value obtained from Box-Behnken design of RSM.

Keywords: Optimization, Cephalosporin C, Acremonium chrysogenum, Solid state fermentation, Artificial neural networks, Response surface methodology

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J Biochem Tech

Volume 1, Issue3, April 2009, Page No. 69-74




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