Intelligent Microbial Growth Monitoring: A Parallel OD₆₀₀ Detection System Based on Artificial Intelligence

Microbial <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{OD}_{\mathbf{600}}$</tex> measurement is a crucial experimental technique in microbiological and molecular biological research, evaluating microbial growth by measuring optical density at 600 nm wavelength. While convenient, this method has limitations. For instance, different microbial species and growth stages may absorb light differently, potentially leading to biases when measuring multiple strains simultaneously. This paper presents an innovative parallel <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{OD}_{\mathbf{600}}$</tex> detection system capable of simultaneously monitoring growth curves of multiple microbial strains. Our research integrates advanced optical systems, precise mechanical control, and artificial intelligence algorithms, not only improving the accuracy and efficiency of bacterial growth measurements but also achieving parallel processing and analysis of multiple samples. We discuss in detail the hardware design, software architecture, and core algorithms, with particular emphasis on the application of machine learning in multi-strain data analysis and prediction. Experimental results demonstrate that this system significantly outperforms traditional methods in measurement accuracy, automation level, multi-sample processing capability, and data analysis.