Innovative fast and low-cost method for the detection of living bacteria based on trajectory

Abstract Detection of pathogens is a major concern in many fields like medicine, pharmaceutics, or agri-food. Most conventional detection methods require skilled staff and specific laboratory equipment for sample collection and analysis or are specific to a given pathogen. Thus, they cannot be easily integrated into a portable device. In addition, the time-to-response, including the sample collection, possible transport to the measurement equipment, and analysis, is often quite long, making real-time impossible. This paper presents a new approach that better fulfills industry needs in terms of integrated real-time wide screening of a large number of samples. It combines optical imaging, object detection and tracking, and machine-learning-based classification. For this study, three of the most common bacteria are considered. For all of them, living bacteria are discriminated from inert and inorganic objects (1µm latex beads), based on their trajectory, with a high degree of confidence. Discrimination between living and dead bacteria of the same species is also achieved. Finally, the method also successfully detects abnormal concentrations of a given bacterium compared to a standard baseline solution. However, there is still room for improvement, these results provide a proof of concept for this technology, which has strong application potential in infection spread prevention.