Magnetic Fe3O4@COF@Ag SERS substrate combined with machine learning algorithms for detection of three quinolone antibiotics: Ciprofloxacin, norfloxacin and levofloxacin

Quinolone antibiotics have good antibacterial properties and are commonly used antibiotics in the dairy in-dustry. Currently, the problem of excessive antibiotics in dairy products is very serious. As an ultra-sensitive detection technology, Surface-Enhanced Raman Scattering (SERS) was applied to the detection of quinolone antibiotics in this work. In order to classify and quantify three antibiotics (Ciprofloxacin, Norfloxacin, Levo-floxacin) with highly similar molecular structures, a combination of magnetic COF-based SERS substrate and machine learning algorithms (PCA-k-NN, PCA-SVM, PCA-Decision Tree) was used. The classification accuracy of the spectral dataset could reach 100% and the results of LOD calculation were: CIP: 5.61 x 10-9M, LEV: 1.44 x 10-8M, NFX: 1.56 x 10-8M. This provides a new method for the detection of antibiotics in dairy products.

Automated summary

In this study, Surface-Enhanced Raman Scattering (SERS) technology was applied to the detection of quinolone antibiotics, and a combination of magnetic COF-based SERS substrate and machine learning algorithms was used for their classification and quantification. The classification accuracy of the spectral dataset reached 100%. This provides a new method for the detection of antibiotics in dairy products.