Optimization of culture conditions for the production of Pleuromutilin from Pleurotus Mutilus using a hybrid method based on central composite design, neural network, and particle swarm optimization

Abstract

This study aims at optimizing the culture conditions (agitation speed, temperature and pH) of the Pleuromutilin production by Pleurotus mutilus. A hybrid methodology including a central composite design (CCD), an artificial neural network (ANN), and a particle swarm optimization algorithm (PSO) was used. Specifically, the CCD and ANN were used for conducting experiments and modeling the non-linear process, respectively. The PSO was used for two purposes: Replacing the standard back propagation in training the ANN (PSONN) and optimizing the process. In comparison to the response surface methodology (RSM) and to the Bayesian regularization neural network (BRNN), PSONN model has shown the highest modeling ability. Under this hybrid approach (PSONN-PSO), the optimum levels of culture conditions were: 242 rpm agitation speed; temperature 26.88 and pH 6.06. A production of 10,074 ± 500 μg/g, which was in very good agreement with the prediction (10,149 μg/g), was observed in verification experiment. The hybrid PSONN-PSO gave a yield of 27.5% greater than that obtained by the hybrid BRNN-PSO. This work shows that the combination of PSONN with the generic PSO algorithm has a good predictability and a good accuracy for bio-process optimization. This hybrid approach is sufficiently general and thus can be helpful for modeling and optimization of other industrial bio-processes.