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Comparative analyses on medium optimization using one-factor-at-a-time, response surface methodology, and artificial neural network for lysine–methionine biosynthesis by Pediococcus pentosaceus RF-1


Citation

Muhamad Nor, Norfarina and Mohamed, Mohd Shamzi and Loh, Teck Chwen and Foo, Hooi Ling and Abdul Rahim, Raha and Tan, Joo Shun and Mohamad, Rosfarizan (2017) Comparative analyses on medium optimization using one-factor-at-a-time, response surface methodology, and artificial neural network for lysine–methionine biosynthesis by Pediococcus pentosaceus RF-1. Biotechnology & Biotechnological Equipment, 31 (5). pp. 935-947. ISSN 1310-2818; ESSN: 1314-3530

Abstract

Optimization strategy that encompassed one-factor-at-a-time (OFAT), response surface methodology (RSM), and artificial neural network method was implemented during medium formulation with specific aim for lysine-methionine biosynthesis employing a newly isolated strain of Pediococcus pentosaceus RF-1. OFAT technique was used in the preliminary screening of factors (molasses, nitrogen sources, fish meal, glutamic acid and initial medium pH) before proceeded to optimization study. Implementation of central composite design of experiment subsequently generated 30 experimental runs based on four factors (molasses, fish meal, glutamic acid, and initial medium pH). From RSM analysis, a quadratic polynomial model can be devoted to describing the relationship between various medium components and responses. It also suggested that using molasses (9.86 g/L), fish meal (10.06 g/L), glutamic acid (0.91 g/L), and initial medium pH (5.30) would enhance the biosynthesis of lysine (15.77 g/L) and methionine (4.21 g/L). Alternatively, a three-layer neural network topography at 4-5-2 predicted a further improvement in the biosynthesis of lysine (16.52 g/L) and methionine (4.53 g/L) by using formulation composed of molasses (10.02 g/L), fish meal (18.00 g/L), and glutamic acid (1.17 g/L) with initial medium pH (4.26), respectively.


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Comparative analyses on medium optimization using one-factor-at-a-time, response surface methodology, and artificial neural network for lysine–methionine biosynthesis by Pediococcus pentosaceus RF-1.pdf

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Additional Metadata

Item Type: Article
Divisions: Faculty of Agriculture
Faculty of Biotechnology and Biomolecular Sciences
DOI Number: https://doi.org/10.1080/13102818.2017.1335177
Publisher: Taylor & Francis
Keywords: Pediococcus pentosaceus RF-1; One-factor-at-a-time; Response surface methodology; Artificial neural network; Lysine; Methionine
Depositing User: Nida Hidayati Ghazali
Date Deposited: 18 Sep 2018 02:31
Last Modified: 08 Apr 2019 04:37
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1080/13102818.2017.1335177
URI: http://psasir.upm.edu.my/id/eprint/61155
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