Improved laccase production from Pleurotus floridanus using deoiled microalgal biomass: statistical and hybrid swarm-based neural networks modeling approach

Dhrisya Chenthamara; Muthusaravanan Sivaramakrishnan; Sankar Ganesh Ramakrishnan; Sivakumar Esakkimuthu; Ram Kothandan; Sadhasivam Subramaniam

doi:10.1007/s13205-022-03404-y

Improved laccase production from Pleurotus floridanus using deoiled microalgal biomass: statistical and hybrid swarm-based neural networks modeling approach

3 Biotech. 2022 Dec;12(12):346. doi: 10.1007/s13205-022-03404-y. Epub 2022 Nov 10.

Authors

Dhrisya Chenthamara¹, Muthusaravanan Sivaramakrishnan², Sankar Ganesh Ramakrishnan¹, Sivakumar Esakkimuthu³, Ram Kothandan⁴, Sadhasivam Subramaniam^{1

5}

Affiliations

¹ Bioprocess and Biomaterials Laboratory, Department of Microbial Biotechnology, Bharathiar University, Coimbatore, India.
² Department of Biotechnology, Mepco Schlenk Engineering College, Sivakasi, India.
³ School of Energy and Power Engineering, Jiangsu University, Jiangsu, 212013 China.
⁴ Department of Biotechnology, Kumaraguru College of Technology, Coimbatore, India.
⁵ Department of Extension and Career Guidance, Bharathiar University, Coimbatore, India.

Abstract

Fungal laccases are versatile biocatalyst and occupy a prominent place in various industrial applications due to its broad substrate specificity. The simplest method to enhance the laccase production is by usage of cheap substrates in the fermentation processes incorporating modeling approaches for optimization. Integrated biorefinery concept is receiving wide popularity by making use of various products from microalgal biomass. The research aimed to identify the potential of deoiled microalgal biomass (DMB), a waste product from algal biorefinery as a nutrient supplement to enhance laccase production in Pleurotus floridanus by submerged fermentation. The maximum production was obtained in the presence of DMB as an additional nutrient supplement and copper sulfate as an inducer. The predictive capabilities of the two methodologies Response Surface Methodology (RSM) and hybrid Particle swarm optimization (PSO)-based Artificial Neural Network (ANN) were compared and validated. The results showed that ANN coupled with PSO predicted with more accuracy with an R ² value of 0.99 than the RSM model with an R ² value of 0.97. The optimized condition as predicted by superior model hybrid PSO-based ANN was glucose (3.51%), DMB (0.545%), pH (4.9), temperature (24.68 ℃) and CuSO₄ (1.35 mM). The experimental laccase activity was 80.45 ± 0.132 U/mL which was 1.3 fold higher than unoptimized condition. This study promotes the usage of DMB as a novel supplement for the improved production of Pleurotus floridanus laccase.

Supplementary information: The online version contains supplementary material available at 10.1007/s13205-022-03404-y.

Keywords: Artificial neural networks; Deoiled microalgal biomass; Laccase; Particle swarm optimization; Response surface methodology.

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