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Design of experiments and artificial neural network linked genetic algorithm for modeling and optimization of L-asparaginase production by Aspergillus terreus MTCC 1782

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Abstract

The sequential optimization strategy for design of an experimental and artificial neural network (ANN) linked genetic algorithm (GA) were applied to evaluate and optimize media component for L-asparaginase production by Aspergillus terreus MTCC 1782 in submerged fermentation. The significant media components identified by Plackett-Burman design (PBD) were fitted into a second order polynomial model (R2 = 0.910) and optimized for maximum L-asparaginase production using a five-level central composite design (CCD). A nonlinear model describing the effect of variables on L-asparaginase production was developed (R2 = 0.995) and optimized by a back propagation NN linked GA. Ground nut oil cake (GNOC) flour 3.99% (w/v), sodium nitrate (NaNO3) 1.04%, L-asparagine 1.84%, and sucrose 0.64% were found to be the optimum concentration with a maximum predicted L-asparaginase activity of 36.64 IU/mL using a back propagation NN linked GA. The experimental activity of 36.97 IU/mL obtained using the optimum concentration of media components is close to the predicted L-asparaginase activity of the ANN linked GA.

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Authors and Affiliations

  1. Bioprocess Engineering Laboratory, Department of Biotechnology, St. Joseph’s College of Engineering, Chennai, 600-119, India

    Baskar Gurunathan

  2. Department of Chemical Engineering, Alagappa College of Technology, Anna University Chennai, Chennai, 600-025, India

    Renganathan Sahadevan

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  1. Baskar Gurunathan
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  2. Renganathan Sahadevan
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Correspondence to Renganathan Sahadevan.

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Gurunathan, B., Sahadevan, R. Design of experiments and artificial neural network linked genetic algorithm for modeling and optimization of L-asparaginase production by Aspergillus terreus MTCC 1782. Biotechnol Bioproc E 16, 50–58 (2011). https://doi.org/10.1007/s12257-010-0119-7

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  • DOI: https://doi.org/10.1007/s12257-010-0119-7

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