Hyper-production optimization of fungal oxidative green enzymes using citrus low-cost byproduct

Débora S.Vilar^a, Clara D.Fernandes^a, Victor R.S.Nascimento^a, Nádia H.Torres^b, Manuela S.Leite^a,b, Ram Naresh Bharagava^c, Muhammad Bilal^d, Giancarlo R.Salazar-Banda^a,b, Katlin I. Barrios Eguiluz^a,b, Luiz Fernando Romanholo Ferreira^a,b

Graduate Program in Process Engineering, Tiradentes University (UNIT), Av. Murilo Dantas, 300, Farolândia, Aracaju, Sergipe 49032-490, Brazil.

Abstract

The use of alternative methods is necessary to improve the production of lignin-modifying enzymes (LMEs) in the biocatalysis scenario. This study demonstrates a new and robust optimization for Lac and MnP enzymes production from Pleurotus sajor-caju induced by pulp wash citrus byproduct. The optimal values determined through response surface methodology (RSM) and artificial neural network coupled to the genetic algorithm (ANN-GA) were agitation rate (180 rpm) and pH (5.5), with a temperature of 28ºC, after 8 days of incubation. The maximum production of Lac and MnP obtained in these conditions using RSM was 307,379.91 IU/L (R2 = 0.9566) and 11,890.20 IU/L (R² = 0.9932), respectively. However, ANN-GA predicted a maximum production of 204,486.96 IU/L and 36,081.02 UI/L, with R² of 0.9903. Thus, under optimized conditions, the agroindustrial pulp wash residue emerges as a promising substrate in the production of LMEs, and therefore, its biotechnological viability enables efficient and sustainable production of bioproducts.

Keywords: Lignin-modifying enzymes, Bioprocesses, Optimization, Surface response methodology, Artificial neural network.