Evaluation of the estimation capability of response surface methodology and artificial neural network for the optimization of bacteriocin-like inhibitory substances production by Lactococcus lactis Gh1

Bacteriocin-like inhibitory substances (BLIS) produced by Lactococcus lactis Gh1 had shown antimicrobial activity against Listeria monocytogenes ATCC 15313. Brain Heart Infusion (BHI) broth is used for the cultivation and enumeration of lactic acid bacteria, but there is a need to improve the curren...

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Main Authors: Roslina Jawan, Sahar Abbasiliasi, Tan, Joo Shun, Mohd Rizal Kapri, Shuhaimi Mustafa, Murni Halim, Arbakariya B. Ariff
Format: Article
Language:English
Published: MDPI 2021
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Online Access:https://eprints.ums.edu.my/id/eprint/42448/1/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42448/
https://doi.org/10.3390/microorganisms9030579
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Institution: Universiti Malaysia Sabah
Language: English
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spelling my.ums.eprints.424482024-12-31T01:17:42Z https://eprints.ums.edu.my/id/eprint/42448/ Evaluation of the estimation capability of response surface methodology and artificial neural network for the optimization of bacteriocin-like inhibitory substances production by Lactococcus lactis Gh1 Roslina Jawan Sahar Abbasiliasi Tan, Joo Shun Mohd Rizal Kapri Shuhaimi Mustafa Murni Halim Arbakariya B. Ariff Q1-295 General QR1-502 Microbiology Bacteriocin-like inhibitory substances (BLIS) produced by Lactococcus lactis Gh1 had shown antimicrobial activity against Listeria monocytogenes ATCC 15313. Brain Heart Infusion (BHI) broth is used for the cultivation and enumeration of lactic acid bacteria, but there is a need to improve the current medium composition for enhancement of BLIS production, and one of the approaches is to model the optimization process and identify the most appropriate medium formulation. Response surface methodology (RSM) and artificial neural network (ANN) models were employed in this study. In medium optimization, ANN (R2 = 0.98) methodology provided better estimation point and data fitting as compared to RSM (R2 = 0.79). In ANN, the optimal medium consisted of 35.38 g/L soytone, 16 g/L fructose, 3.25 g/L sodium chloride (NaCl) and 5.40 g/L disodium phosphate (Na2HPO4). BLIS production in optimal medium (717.13 ± 0.76 AU/mL) was about 1.40-fold higher than that obtained in nonoptimised (520.56 ± 3.37 AU/mL) medium. BLIS production was further improved by about 1.18 times higher in 2 L stirred tank bioreactor (787.40 ± 1.30 AU/mL) as compared to that obtained in 250 mL shake flask (665.28 ± 14.22 AU/mL) using the optimised medium. MDPI 2021 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/42448/1/FULL%20TEXT.pdf Roslina Jawan and Sahar Abbasiliasi and Tan, Joo Shun and Mohd Rizal Kapri and Shuhaimi Mustafa and Murni Halim and Arbakariya B. Ariff (2021) Evaluation of the estimation capability of response surface methodology and artificial neural network for the optimization of bacteriocin-like inhibitory substances production by Lactococcus lactis Gh1. Microorganisms, 9. pp. 1-22. https://doi.org/10.3390/microorganisms9030579
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
topic Q1-295 General
QR1-502 Microbiology
spellingShingle Q1-295 General
QR1-502 Microbiology
Roslina Jawan
Sahar Abbasiliasi
Tan, Joo Shun
Mohd Rizal Kapri
Shuhaimi Mustafa
Murni Halim
Arbakariya B. Ariff
Evaluation of the estimation capability of response surface methodology and artificial neural network for the optimization of bacteriocin-like inhibitory substances production by Lactococcus lactis Gh1
description Bacteriocin-like inhibitory substances (BLIS) produced by Lactococcus lactis Gh1 had shown antimicrobial activity against Listeria monocytogenes ATCC 15313. Brain Heart Infusion (BHI) broth is used for the cultivation and enumeration of lactic acid bacteria, but there is a need to improve the current medium composition for enhancement of BLIS production, and one of the approaches is to model the optimization process and identify the most appropriate medium formulation. Response surface methodology (RSM) and artificial neural network (ANN) models were employed in this study. In medium optimization, ANN (R2 = 0.98) methodology provided better estimation point and data fitting as compared to RSM (R2 = 0.79). In ANN, the optimal medium consisted of 35.38 g/L soytone, 16 g/L fructose, 3.25 g/L sodium chloride (NaCl) and 5.40 g/L disodium phosphate (Na2HPO4). BLIS production in optimal medium (717.13 ± 0.76 AU/mL) was about 1.40-fold higher than that obtained in nonoptimised (520.56 ± 3.37 AU/mL) medium. BLIS production was further improved by about 1.18 times higher in 2 L stirred tank bioreactor (787.40 ± 1.30 AU/mL) as compared to that obtained in 250 mL shake flask (665.28 ± 14.22 AU/mL) using the optimised medium.
format Article
author Roslina Jawan
Sahar Abbasiliasi
Tan, Joo Shun
Mohd Rizal Kapri
Shuhaimi Mustafa
Murni Halim
Arbakariya B. Ariff
author_facet Roslina Jawan
Sahar Abbasiliasi
Tan, Joo Shun
Mohd Rizal Kapri
Shuhaimi Mustafa
Murni Halim
Arbakariya B. Ariff
author_sort Roslina Jawan
title Evaluation of the estimation capability of response surface methodology and artificial neural network for the optimization of bacteriocin-like inhibitory substances production by Lactococcus lactis Gh1
title_short Evaluation of the estimation capability of response surface methodology and artificial neural network for the optimization of bacteriocin-like inhibitory substances production by Lactococcus lactis Gh1
title_full Evaluation of the estimation capability of response surface methodology and artificial neural network for the optimization of bacteriocin-like inhibitory substances production by Lactococcus lactis Gh1
title_fullStr Evaluation of the estimation capability of response surface methodology and artificial neural network for the optimization of bacteriocin-like inhibitory substances production by Lactococcus lactis Gh1
title_full_unstemmed Evaluation of the estimation capability of response surface methodology and artificial neural network for the optimization of bacteriocin-like inhibitory substances production by Lactococcus lactis Gh1
title_sort evaluation of the estimation capability of response surface methodology and artificial neural network for the optimization of bacteriocin-like inhibitory substances production by lactococcus lactis gh1
publisher MDPI
publishDate 2021
url https://eprints.ums.edu.my/id/eprint/42448/1/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42448/
https://doi.org/10.3390/microorganisms9030579
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