Monitoring the Bacterial Response to Antibiotic and Time Growth Using Near-infrared Spectroscopy Combined with Machine Learning

In this study, Escherichia coli was chosen as the model microorganism to evaluate its response (e.g., growth) to a commercial antibiotic—tetracycline. Thus, the objective of this work was to explore the ability of NIR data combined with machine learning tools (e.g., partial least squares discriminant analysis) to monitor the response and growth of Escherichia coli cultured with different concentrations of tetracycline (ranging from 0 to 50 μg/mL). This study demonstrated a novel method capable of analyzing samples of a complex matrix, while still contained in a 96-well plate.