One of the leading limiting factors for wider industrial production and commercialization of microbial biopesticides refers to the high costs of cultivation media. The selection of alternative sources of macronutrients crucial for the growth and metabolic activity of the producing microorganism is a necessary phase of the bioprocess development. Gaining a better understanding of the influence of the medium composition on the biotechnological production of biocontrol agents is enabled through bioprocess modelling and optimization. In the present study, after the selection of optimal carbon and nitrogen sources, two modelling approaches were applied to mathematically describe the behavior of the examined bioprocess-the production of biocontrol agents effective against aflatoxigenic Aspergillus flavus strains. The modelling was performed using four independent variables: cellulose, urea, ammonium sulfate and dipotassium phosphate, and the selected response was the inhibition-zone diameter. After the comparison of the results generated by the Response Surface Methodology (RSM) and the Artificial Neural Network (ANN) approach, the first model was chosen for the further optimization step due to the better fit of the experimental results. As the final investigation step, the optimal cultivation medium composition was defined (g/L): cellulose 5.0, ammonium sulfate 3.77, dipotassium phosphate 0.3, magnesium sulfate heptahydrate 0.3.
Keywords: ANN; RSM; biocontrol; bioprocess; modelling; optimization.