Machine Learning-Enhanced Bacteria Detection Using a Fluorescent Sensor Array with Functionalized Graphene Quantum Dots.

Pathogenic bacteria are the source of many serious health problems, such as foodborne diseases and hospital infections. Timely and accurate detection of these pathogens is of vital significance for disease prevention, control of epidemic spread, and protection of public health security. Rapid identification of pathogenic bacteria has become a research focus in recent years.

Machine learning-driven fluorescent sensor array using aqueous CsPbBr perovskite quantum dots for rapid detection and sterilization of foodborne pathogens.

With the growing global concern over food safety, the rapid detection and disinfection of foodborne pathogens have become critical in public health. This study presents a novel machine learning-driven fluorescent sensor array utilizing aqueous CsPbBr perovskite quantum dots (PQDs) for the rapid identification and eradication of foodborne pathogens. The relative signal intensity changes (ΔRGB) generated by the sensor array were analyzed using the machine learning algorithm-Support Vector Machine (SVM).