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 Vytla, G. S. Vijay, Kannan Natarajan

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number259
Journal3 Biotech
Volume9
Issue number7
DOIs
Publication statusPublished - 01-07-2019

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tannase
response surface methodology
genetic algorithm
artificial neural network
neural networks
substrate
aeration
fermentation
moisture content
Fermentation
modeling
Neural Networks (Computer)
Bacillus
fold
water content
prediction
reactor
triphala
experiment
Research

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Environmental Science (miscellaneous)
  • Agricultural and Biological Sciences (miscellaneous)

Cite this

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title = "Modeling and optimization of tannase production with Triphala in packed bed reactor by response surface methodology, genetic algorithm, and artificial neural network",
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.",
author = "Subbalaxmi Selvaraj and Vytla, {Ramachandra Murty} and Vijay, {G. S.} and Kannan Natarajan",
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Modeling and optimization of tannase production with Triphala in packed bed reactor by response surface methodology, genetic algorithm, and artificial neural network. / Selvaraj, Subbalaxmi; Vytla, Ramachandra Murty; Vijay, G. S.; Natarajan, Kannan.

In: 3 Biotech, Vol. 9, No. 7, 259, 01.07.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Modeling and optimization of tannase production with Triphala in packed bed reactor by response surface methodology, genetic algorithm, and artificial neural network

AU - Selvaraj, Subbalaxmi

AU - Vytla, Ramachandra Murty

AU - Vijay, G. S.

AU - Natarajan, Kannan

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