Trichromatic ratiometric fluorescent sensor based on machine learning and smartphone for visual and portable monitoring of tetracycline antibiotics

Abstract

In this work, a tri-color ratiometric fluorescent optical device integrated with machine learning-assisted smartphones is designed for the visual monitoring of tetracycline antibiotics. The trichromatic sensing probe consists of blue emitting carbon dots (BCDs) at 420 nm, and red-emitting bovine serum albumin-protected copper nanoclusters (BSA-Cu NCs) at 635 nm, as well as an emission peak at 520 nm, rises in the presence of tetracycline antibiotics (TCs), therefore, a three emission system is formed. BCDs/BSA-Cu NCs present three emission responses to TCs, which are based on the internal filtering effect (IFE) and the sensitization mechanisms with BSA. The reaction mechanism was verified by density functional theory (DFT) and a series of characterizations, such as infrared spectrum, ultraviolet absorption spectrum, and fluorescence lifetime. Interestingly, the fluorescence intensity of BCDs was quenched by TCs, and due to the sensitization between BSA and TCs, a green fluorescence rose at 520 nm, while the peak of BSA-Cu NCs remained stable, resulting in a significant color shift from red to cyan. Therefore, the ratiometric fluorescence intensity of I(635+520)/I420 can be used to determine TCs, and the colors change can be used to identify the quantitative analysis. In addition, the deep learning of the YOLO v3 algorithm assists the integrated tri-color portable optical device of smartphones, which can realize the visual detection of TCs. In general, the sensing system has many advantages, such as good stability, specificity, and rich color changes, which provide help for the visual detection of tetracycline residues.