Exclusive Biosynthesis of Pullulan Using Taguchi's Approach and Decision Tree Learning Algorithm by a Novel Endophytic Aureobasidium pullulans Strain.

Abstract

Pullulan is a biodegradable, renewable, and environmentally friendly hydrogel biopolymer, with potential uses in food, medicine, and cosmetics. New endophytic Aureobasidium pullulans (accession number; OP924554) was used for the biosynthesis of pullulan. Innovatively, the fermentation process was optimized using both Taguchi's approach and the decision tree learning algorithm for the determination of important variables for pullulan biosynthesis. The relative importance of the seven tested variables that were obtained by Taguchi and the decision tree model was accurate and followed each other's, confirming the accuracy of the experimental design. The decision tree model was more economical by reducing the quantity of medium sucrose content by 33% without a negative reduction in the biosynthesis of pullulan. The optimum nutritional conditions (g/L) were sucrose (60 or 40), K2HPO4 (6.0), NaCl (1.5), MgSO4 (0.3), and yeast extract (1.0) at pH 5.5, and short incubation time (48 h), yielding 7.23% pullulan. The spectroscopic characterization (FT-IR and 1H-NMR spectroscopy) confirmed the structure of the obtained pullulan. This is the first report on using Taguchi and the decision tree for pullulan production by a new endophyte. Further research is encouraged for additional studies on using artificial intelligence to maximize fermentation conditions.