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3 Biotech

, 9:259 | Cite as

Modeling and optimization of tannase production with Triphala in packed bed reactor by response surface methodology, genetic algorithm, and artificial neural network

  • Subbalaxmi Selvaraj
  • Ramachandra Murty VytlaEmail author
  • G. S. Vijay
  • Kannan Natarajan
Original Article
  • 19 Downloads

Abstract

In this research, optimization of the production medium to enhance tannase production by Bacillus gottheilii M2S2 in laboratory-scale packed bed reactor was studied. Amount of substrate Triphala, moisture content, aeration rate, and fermentation period was chosen for optimization study. During one variable at a time optimization, the highest tannase activity of 0.226 U/gds was shown with Triphala as a substrate at the fermentation period of 32 h. Furthermore, the optimum conditions predicted by response surface methodology (RSM) and genetic algorithm (GA) were found to be 11.532 g of substrate Triphala, 47.071% of the moisture content, and 1.188 L/min of an aeration rate with uppermost tannase activity of 0.262 U/gds. In addition, the single hidden layer feedforward neural network (SLFNN) and the radial basis function neural network (RBFNN) of an artificial neural network (ANN) were adopted to compare the prediction performances of the RSM and GA. It revealed that the ANN models (SLFNN, R2 = 0.9930; and RBFNN, R2 = 0.9949) were better predictors than the RSM (R2 = 0.9864). Finally, the validation experiment exhibited 0.265 U/gds of tannase activity at the optimized conditions, which is an 11-fold increase compared to unoptimized media in shake flask.

Keywords

Tannase Packed bed reactor Genetic algorithm Artificial neural network Response surface methodology Triphala 

Notes

Acknowledgements

The authors are grateful to the Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, India, for providing the facilities to carry out the research work.

Compliance with ethical standards

Conflict of interest

The authors certify that no actual or potential conflicts of interest in relation to this article exist.

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Copyright information

© King Abdulaziz City for Science and Technology 2019

Authors and Affiliations

  • Subbalaxmi Selvaraj
    • 1
  • Ramachandra Murty Vytla
    • 1
    Email author
  • G. S. Vijay
    • 2
  • Kannan Natarajan
    • 1
  1. 1.Department of Biotechnology, Manipal Institute of TechnologyManipal Academy of Higher EducationManipalIndia
  2. 2.Department of Mechanical Engineering, Manipal Institute of TechnologyManipal Academy of Higher EducationManipalIndia

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