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

Assessing and monitoring the growth and response of bacteria to antibiotics is of crucial importance in research laboratories, as well as in food, environment, medical, and pharmaceutical industrial applications. In this study, 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 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. This work will pave the way as a new machine learning method to detect resistance changes in microorganisms without the laborious and, in some cases, time-consuming protocols currently in use in research and by the industry.