RESEARCH ARTICLE
Optimization of Process Parameters for Cholesterol Oxidase Production by Streptomyces Olivaceus MTCC 6820
Shraddha Sahu1, Shailendra Singh Shera1, Rathindra Mohan Banik1, *
Article Information
Identifiers and Pagination:
Year: 2019Volume: 13
First Page: 47
Last Page: 58
Publisher ID: TOBIOTJ-13-47
DOI: 10.2174/1874070701913010047
Article History:
Received Date: 22/01/2019Revision Received Date: 12/03/2019
Acceptance Date: 26/03/2019
Electronic publication date: 30/04/2019
Collection year: 2019
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
Streptomyces olivaceus MTCC 6820 is a potent microorganism for cholesterol oxidase (ChOx) production through the submerged fermentation process. Statistical optimization of the process parameters for submerged fermentation enhances the production of enzymes.
Objective:
This work is aimed to optimize the culture conditions for the fermentative production of cholesterol oxidase by Streptomyces olivaceus MTCC 6820 using combined Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques.
Methods:
The ChOx production (U/ml) was modeled and optimized as a function of six independent variables (culture conditions) using RSM and ANN.
Results:
ChOx production enhanced 2.2 fold, i.e 1.9 ± 0.21 U/ml under unoptimized conditions to 4.2 ± 0.51 U/ml after the optimization of culture conditions. Higher coefficient of determination (R2 = 97.09 %) for RSM and lower values of MSE (0.039) and MAPE (3.46 %) for ANN proved the adequacy of both the models. The optimized culture conditions predicted by RSM vs. ANN were pH (7.5), inoculum age (48 h), inoculum size (11.25 % v/v), fermentation period (72 h), incubation temperature (30°C) and shaking speed (175 rpm).
Conclusion:
The modeling, optimization and prediction abilities of both RSM and ANN methodologies were compared. The values of Pearson correlation coefficient (r) (ANN0.98 > RSM0.95), regression coefficient (R2) between experimental activity, RSM and ANN predicted ChOx activity, respectively (ANN0.96 > RSM0.90) and Absolute Average Deviation (AAD) for (ANN3.46% < RSM9.87%) substantiated better prediction ability of ANN than RSM. These statistical values indicated the superiority of ANN in capturing the non-linear behavior of the system.
