Artificial neural network-based modelling of optimized experimental study of xylanase production by Penicillium citrinum xym2

Abstract

The industrial production of enzymes is generally optimized by one-factor-at-a-time (OFAT) approach. However, enzyme production by the method involves submerged or solid-state fermentation, which is laborious and time-consuming and it does not consider interactions among process variables. Artificial neural network (ANN) offers enormous potential for modelling biochemical processes and it allows rational prediction of process variables of enzyme production. In the present work, ANN has been used to predict the experimental values of xylanase production optimized by OFAT. This makes the reported ANN model to predict further optimal values for different input conditions. Both single hidden layered (6-3-1) and double hidden layered (6-12-12-1) were able to closely predict the actual values with MSE equals to 0.004566 and 0.002156, respectively. The study also uses multiple linear regression (MLR) analysis to calculate and compare the outcome with ANN predicted xylanase activity, and to establish a parametric sensitivity.