Response Surface Methodology (RSM) and Artificial Neural Network (ANN) based optimization of cellulase production using Aspergillus niger in solid state fermentation of seed pods of Bombax ceiba

Abstract

Abstract The bioconversion of lignocellulosic materials into valuable chemicals demands extensive research on production of cellulase from bacterial and fungal sources. Various Aspergillus species are known for cellulase production. The current study aims to the production and optimization of CMCase and FPase using Bombax ceiba as a substrate from Aspergillus niger under solid state fermentation. The response surface methodology using Box-Behnken design (BBD) and Artificial neural network have been employed to maximize the cellulase activity. The BBD utilized three factors including incubation period, moisture content and inoculum size at three levels to explore the enhanced cellulase activity. The maximum CMCase and FPase activities were found to be 84.5 IU/ml/min and 78.89 IU/ml/min respectively. Moreover, 7 days of incubation duration, 2ml inoculum volume and 60% moisture content were found to be as optimal conditions for CMCase activity. On the other hand, optimal moisture content for FPase was 20%.The results showed that model was highly significant with R2 values for CMCase and FPase were obtained as 97.27% and 97.54% by RSM analysis respectively.While R2 values from ANN were 97.062 and 99.231% for CMCase and FPase respectively. It is evident from the R2 values that ANN capture the non linear relationship between variables more efficiently as compare to RSM. The sensitivity analysis predicts that incubation duration is most critical parameter in cellulase production. Overall, the findings support the utilization of agricultural residues for industrial enzyme production, helping to deal both economic feasibility and environmental sustainability.