Abstract

In this study, cost-effective substrates such as cassava starch, corn steep liquor (CSL) and soybean meal hydrolysate (SMH) were used for pullulan production by Aureobasidium pullulans CCTCC M 2012259. The medium was optimized using response surface methodology (RSM) and artificial neural network (ANN) approaches, and analysis of variance indicated that the ANN model achieved higher prediction accuracy. The optimal medium predicted by ANN was used to produce high molecular weight pullulan in high yield. SMH substrates increased both biomass and pullulan titer, while CSL substrates maintained higher pullulan molecular weight. Results of kinetic parameters, key enzyme activities and intracellular uridine diphosphate glucose contents revealed the physiological mechanism of changes in pullulan titer and molecular weight using different substrates. Economic analysis of batch pullulan production using different substrates was performed, and the cost of nutrimental materials for CSL and SMH substrates was decreased by 46.1% and 49.9%, respectively, compared to the control using glucose and yeast extract as substrates, which could improve the competitiveness of pullulan against other polysaccharides in industrial applications.

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