Multi-dimensional Optimization of High-density Lactobacillus gasseri GU-G23 Culture
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Abstract:
To increase the biomass of probiotic Lactobacillus gasseri GU-G23 and identify factors associated with a high-density culture, the main nutritional factors required using a single-factor experiment and Plackett-Burman design were established. The three key factors comprised fish peptone, tryptone, and triammonium citrate. The training sample for the machine learning model was generated according to the response surface experimental design. The random forest regression (RFR) and radial basis function neural network (RBF) models were used to predict their culture medium formulas. The coefficient of determination (R-squared, R2), mean absolute deviation (MAE), mean-square error (MSE), and mean absolute percentage error (MAPE) were adopted as model evaluation metrics. Comparisons revealed that RBF exhibited superior predictive performance. Subsequently, a combination of RBF neural network and genetic algorithm (GA) was selected to optimize the main components of the culture medium. The optimized formula comprised 29.89 g/L fish peptone, 23.33 g/L tryptone, 4.34 g/L triammonium citrate, 15.00 g/L sucrose, 15.00 g/L xylo-oligosaccharides, 5.00 g/L sodium acetate, 0.40 g/L dipotassium hydrogen phosphate, 0.58 g/L magnesium sulfate heptahydrate, 0.29 g/L manganese sulfate monohydrate, and 1.00 g/L Tween-80. Under this medium composition, the number of viable bacterial cells reached 5.21×109 CFU/mL, which was 4.57 times higher than that before optimization. This study provides a new approach for the optimization of microbial high-density culture medium prediction.
