Deep Learning Sensing Platform for Simultaneous Detection of Kanamycin and Kanamycin B Based on Dual‐Recognition and Catalytic Modulation Mechanism of Aptamer‐Functionalized MIL‐101 Nanozyme

ABSTRACT Extensive antibiotic use has raised concerns over residues in food, necessitating rapid and accurate detection. This study integrates smartphone‐assisted deep learning with aptamer‐functionalized MIL‐101(Fe) nanozymes to build a dual‐recognition biosensor capable of simultaneously identifying and quantifying kanamycin (KAN) and kanamycin B (KMB). MIL‐101(Fe) exhibited strong adsorption energies and high Langmuir‐fitted capacities (28.39 mg/g for KAN; 31.36 mg/g for KMB), indicating monolayer binding at Fe─OH sites that partially suppressed catalytic activity. Aptamer modification enabled selective molecular recognition and accelerated electron transfer, synergistically enhancing nanozyme performance. Coupled with a ResNet‐based multitask model, smartphone imaging achieved accurate classification of single and mixed samples and precise quantification over 5–1000 nM. Tests in honey and beef showed recoveries of 84.71%–111.50%, confirming stability and practicality. Overall, the aptamer‐enhanced MIL‐101(Fe) nanozyme combined with deep learning provides a sensitive, portable approach for on‐site monitoring of antibiotic residues in food.