The SERS method based on the COF-ag substrate, combined with machine learning, is used for the detection of tetracycline and oxytetracycline in milk.

Tetracycline antibiotics, valued for potent antibacterial effects, are widely used in livestock but raise concerns over unsafe residues in milk. In this study, a surface-enhanced Raman spectroscopy (SERS) method based on an amino-functionalized covalent organic framework (COF) was developed for the detection of trace tetracycline (TTC) and oxytetracycline (OTC) in milk. The COF material acted as a selective ligand, effectively mitigating interference from the milk matrix. In addition, hydrogen bonding between the COF and the antibiotic molecules contributed to the chemical enhancement of the Raman signal. The proposed method achieved low detection limits of 0.05 μg/L for TTC and 0.07 μg/L for OTC. Owing to their structural similarity, principal component analysis was employed for dimensionality reduction, combined with a support vector machine classification algorithm, which enabled accurate discrimination of the antibiotics with a classification accuracy of 100 % based on the spectral data.