Development of the High-Cell-Density Fermentation Process for Lactiplantibacillus plantarum JD2 Based on Bayesian Optimization

To achieve high-cell-density cultivation of Lactiplantibacillus plantarum JD2, the fermentation process was systematically optimized by combining single factor experiments with Bayesian optimization. Key influencing factors were identified using single-factor experiments and then Bayesian initial dataset was constructed. On this basis, the Gaussian process regression model was used as surrogate model, and the upper confidence bound acquisition functions was adopted for multiple rounds of interactive optimization, and the fermentation process parameters were optimized. The results showed that the optimized medium components were sucrose 21.10 g/L, peptone FP400 13.30 g/L, yeast extract FM888 38.30 g/L, K2HPO4 2.00 g/L, diammonium hydrogen citrate 2.00 g/L, sodium acetate 5.00 g/L, MgSO4 0.20 g/L, MnSO4 0.05 g/L, FeSO4 0.05 g/L, CaSO4 0.10 g/L, and Tween 80 1.00 g/L. The optimized fermentation conditions were as follows: initial pH 6.5, inoculum 2%, fermentation temperature 33 ℃, rotation speed 50 r/min, and culture time 24 h. Under these conditions, the viable cell count reached 7.03 × 109 CFU/mL, with an increase of 269% compare to that of before optimization. These results confirmed the high efficiency of Bayesian optimization in fermentation process development and provided both data support and practical technical scheme for the industrial application of L. plantarum JD2.