Fungal biosynthesis of lignin-modifying enzymes from pulp wash and Luffa cylindrica for azo dye RB5 biodecolorization using modeling by response surface methodology and artificial neural network
Clara DouradoFernandesa, Victor Ruan SilvaNascimentoa, Diego Batista Menesesb, Débora S.Vilara, Nádia Hortense Torresa,c, Manuela Souza Leitea,c, José RobertoVega Baudritb,d, Muhammad Bilale, Hafiz M.N.Iqbalf, Ram Naresh Bharagavag, Silvia Maria Eguesa,c, Luiz Fernando Romanholo Ferreiraa,c
Graduate Program in Process Engineering, Tiradentes University, Murilo Dantas Avenue, 300, Farolândia, 49032-490, Aracaju, Sergipe, Brazil.
This study demonstrates the evaluation between the artificial neural network technique coupled to the genetic algorithm (ANN-GA) and the response surface methodology (RSM) for prediction of Reactive Black 5 (RB5) decolorization by crude enzyme from Pleurotus. sajor-caju. Fungal lignin-modifying enzymes (FLME) were synthesized using pulp wash (PW) as an inducing substrate, and L. cylindrica (L.C) for cell immobilization. When grown in PW, the fungus showed higher Lac activity (126.5 IU. mL-1), whereas when immobilized a higher MnP activity was achieved (22.79 IU. mL−1), but both methods were capable of decolorizing the dye in about 89.4 % and 75 %, respectively. This indicates applicability of PW as an alternative substrate for FLME induction and viability of immobilization for MnP synthesis. For RB5 decolorization, the action of the crude enzyme extract was considered as a function of pH, dye concentration, temperature, and reaction time. The models are well adjusted to predict the efficiency of biodecolorization, with no statistical difference between ANN-GA and RSM, which indicates potential for green enzymes prospecting application in bioprocess industry.
Keywords: Reactive Black 5, Pleurotus sajor-caju, Immobilization, Genetic algorithm, Green enzymes.