Ratiometric fluorescence sensor based on deep learning for rapid and user-friendly detection of tetracycline antibiotics

The detection of tetracycline antibiotics (TCs) in food holds great significance in minimizing their absorption within the human body. Hence, this study aims to develop a rapid, convenient, real-time, and accurate detection method for detecting antibiotics in an authentic market setting. A colorimetric fluorescence sensor was devised for tetracycline detection utilizing PVA aerogels as the substrate. Its operating principle is based on the IFE effect and antenna effect. A detection device is designed to capture fluorescence images while deep learning was employed to aid in the detection process. The sensor exhibits high responsiveness with a mere 60-s requirement for detection and demonstrates substantial color changes(blue to red), achieving 99% accuracy within the range of 10–100 μM with the assistance of deep learning (Resnet18). Real sample simulation tests yielded recovery rates between 95% and 130%. Overall, the proposed strategy proved to be a simple, portable, reliable, and responsive solution for rapid real-time TCs detection in food samples.