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; Joo Shun Tan; Mohd Rizal Kapri; Shuhaimi Mustafa; Murni Halim; Arbakariya B Ariff

doi:10.3390/microorganisms9030579

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. 2021 Mar 12;9(3):579. doi: 10.3390/microorganisms9030579.

Authors

Roslina Jawan^{1

2}, Sahar Abbasiliasi³, Joo Shun Tan⁴, Mohd Rizal Kapri¹, Shuhaimi Mustafa^{3

5}, Murni Halim^{1

6}, Arbakariya B Ariff^{1

6}

Affiliations

¹ Bioprocessing and Biomanufacturing Research Centre, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia.
² Biotechnology Programme, Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, Malaysia.
³ Halal Products Research Institute, Universiti Putra Malaysia, Serdang 43400, Malaysia.
⁴ School of Industrial Technology, Universiti Sains Malaysia, Gelugor 11800, Malaysia.
⁵ Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia.
⁶ Department of Bioprocess Technology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia.

Abstract

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.

Keywords: Lactococcus lactis Gh1; artificial neural network; bacteriocin-like inhibitory substances; optimization; response surface methodology.