ABSTRACT
The exopolysaccharide production from blueberry juice fermented were investigated. The highest exopolysaccharide yield of 2.2 ± 0.1 g/L (increase by 32.5 %) was reached under the conditions of temperature 26.5 °C, pH 5.5, inoculated quantity 5.4 %, and glucose addition 9.1 % using the artificial neural network and genetic algorithm. Under the optimal conditions, the viable cell counts and total acids were increased by 2.0 log CFU/mL and 1.6 times, respectively, while the content of phenolics and anthocyanin was decreased by 9.26 % and 7.86 %, respectively. The changes of these components affected the exopolysaccharide biosynthesis. The absorption bands of -OH and -CH associated with the main functional groups of exopolysaccharide were detected by Visible near-infrared spectroscopy. The prediction model based on spectrum results was constructed. Competitive adaptive reweighted sampling and the random forest were used to enhance the model's prediction performance with the value of RC = 0.936 and RP = 0.835, indicating a good predictability of exopolysaccharides content during fermentation.
