AUTHOR=Li Yu-han , Yao Su-hang , Zhou Yan , He Xiu-lan , Yuan Zhe-ming , Hu Qiu-long , Shen Cheng-wen , Li Xin , Chen Yuan 

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

JOURNAL=Frontiers in Microbiology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1556322

DOI=10.3389/fmicb.2025.1556322

ISSN=1664-302X

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.