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

Abstract

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.

Keywords: Acinetobacter calcoaceticus; PQQ; formulation optimization; support vector regression; uniform design.

Figure 1

Frequency distribution of the six retained factors. (A–F) represent the frequency distributions of yeast powder, anhydrous ammonium sulfate, L-glutamic acid, L-tyrosine, temperature, and pH, respectively. The left y-axis of each subgraph corresponds to the predicted PQQ production values (scatterplot), while the right y-axis represents the frequency of occurrence for each level (blue line plot).