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Media optimization for biosurfactant production by Rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach

  • Original Paper
  • Published:
Journal of Industrial Microbiology & Biotechnology

Abstract

This paper entails a comprehensive study on production of a biosurfactant from Rhodococcus erythropolis MTCC 2794. Two optimization techniques—(1) artificial neural network (ANN) coupled with genetic algorithm (GA) and (2) response surface methodology (RSM)—were used for media optimization in order to enhance the biosurfactant yield by Rhodococcus erythropolis MTCC 2794. ANN and RSM models were developed, incorporating the quantity of four medium components (sucrose, yeast extract, meat peptone, and toluene) as independent input variables and biosurfactant yield [calculated in terms of percent emulsification index (% EI24)] as output variable. ANN-GA and RSM were compared for their predictive and generalization ability using a separate data set of 16 experiments, for which the average quadratic errors were ~3 and ~6%, respectively. ANN-GA was found to be more accurate and consistent in predicting optimized conditions and maximum yield than RSM. For the ANN-GA model, the values of correlation coefficient and average quadratic error were ~0.99 and ~3%, respectively. It was also shown that ANN-based models could be used accurately for sensitivity analysis. ANN-GA-optimized media gave about a 3.5-fold enhancement in biosurfactant yield.

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Acknowledgements

The authors would like to thank Dr. S. S. Bhagwat (ICT, Mumbai) for his help in measurement of surface tension and CMC. BKV would like to acknowledge the SRF grant from CSIR-India. RMJ would like to thank DST-India for the financial support.

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Authors and Affiliations

  1. Chemical Engineering and Process Development Division, National Chemical Laboratory, Pune, 411008, India

    Moumita P. Pal, Bhalchandra K. Vaidya, Renuka M. Joshi, Sanjay N. Nene & Bhaskar D. Kulkarni

  2. Food Engineering and Technology Department, Institute of Chemical Engineering, Matunga, Mumbai, 400019, India

    Kiran M. Desai

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  1. Moumita P. Pal
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Correspondence to Sanjay N. Nene.

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Pal, M.P., Vaidya, B.K., Desai, K.M. et al. Media optimization for biosurfactant production by Rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach. J Ind Microbiol Biotechnol 36, 747–756 (2009). https://doi.org/10.1007/s10295-009-0547-6

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  • DOI: https://doi.org/10.1007/s10295-009-0547-6

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