Dual-mode detection of tetracyclines in animal-derived food samples based on MOF-on-MOF heterostructure fluorescent...

Tetracyclines (TCs) that accumulate in food enter the human body through the food chain, leading to drug resistance and health risks. This study synthesized a heterostructure fluorescent sensor, Zn-TCPP-MOF/NH₂-MIL-53(Al) (abbreviated ZTA) for TCs detection. A dual-mode detection system was established by integrating fluorescence spectroscopy with machine learning algorithms and fluorescence imaging with YOLOv5, enabling precise detection of TCs. The π-π stacking reduced ZTA electron density to promote electron transfer, inducing specific recognition of TCs with a low detection limit of 0.012 μmol/L. Machine learning algorithms of k-means clustering, hierarchical cluster analysis, support vector machine, random forest, logistic regression, and decision tree achieved 100.0%, and YOLOv5 reached 99.8% precision for TCs identification. The ZTA sensor achieved recovery rates between 95.40%-108.50% for TCs detection in animal-derived foods. This study facilitated the design of a MOF-on-MOF heterostructure sensor and provided an intelligent detection strategy for the rapid on-site monitoring of antibiotic residues.