An Efficient High-Throughput Screening of High Gentamicin-Producing Mutants Based on Titer Determination Using an Integrated Computer-Aided Vision Technology and Machine Learning.

The "design-build-test-learn" (DBTL) cycle has been adopted in rational high-throughput screening to obtain high-yield industrial strains. However, the mismatch between build and test slows the DBTL cycle due to the lack of high-throughput analytical technologies. In this study, a highly efficient, accurate, and noninvasive detection method of gentamicin (GM) was developed, which can provide timely feedback for the high-throughput screening of high-yield strains. First, a self-made tool was established to obtain data sets in 24-well plates based on the color of the cells. Subsequently, the random forest (RF) algorithm was found to have the highest prediction accuracy with an R 2 value of 0.98430 for the same batch. Finally, a stable genetically high-yield strain (998 U/mL) was successfully screened out from 3005 mutants, which was verified to improve the titer by 72.7% in a 5 L bioreactor. Moreover, the verified new data sets were updated on the model database in order to improve the learning ability of the DBTL cycle.