Enhancing PQQ production in Acinetobacter calcoaceticus through uniform design and support vector regression

A novel machine learning-assisted approach for formula optimization, termed UD-SVR, is introduced by combining uniform design with support vector regression. This method was employed to optimize both the formulation and fermentation conditions for pyrroloquinoline quinone (PQQ) production by Acinetobacter calcoaceticus . In just two rounds of 66 experimental treatments, UD-SVR effectively optimized a formulation involving eight factors at the shake-out level scale, enhancing PQQ production from 43.65 mg/L to 73.40 mg/L—an impressive 68.15% increase. Notably, the optimized formulation is also cost-effective, featuring minimized consumption of pivotal elements like carbon and nitrogen sources. The machine learning-supported UD-SVR method presents an inclusive resolution for optimizing experimental designs and analyses in multi-factor, multi-level formulations, characterized by robust guidance, lucid interpretability, and heightened efficiency in optimization.