Enhancement of production of l-methioninase after optimizing culture condition of Pseudomonas stutzeri using artificial neural network

Abstract

l-Methioninase isolated from microbial strain has currently great demand in pharmaceutical and food sectors. In this study, Pseudomonas stutzeri was explored as new novel bacterial strain for producing l-methioninase. The optimization of its cultural condition would be crucial for scale up of its production at large extent. Response surface methodology (RSM) was first applied to obtain central composite design data and artificial neural networking (ANN) was further applied to analyze these data. ANN had analyzed these data more accurately as compared to RSM model with lower value of absolute average deviation (0.25% ANN < 1.22% RSM), low value of mean square error (MSE = 3.6%). The model was verified by carrying experiments at optimum parameters like inoculum volume (1 ml), pH of media (7.5), incubation period (20 h), incubation temperature (37 °C) and agitation speed (110 rpm). The experimental activity of l-methioninase was obtained as 285.63 U/l. The study of growth and production kinetic at these optimum cultural conditions showed non-growth associated behavior with improved production of l-methioninase. © 2022, The Author(s) under exclusive licence to Society for Plant Research.