Magnetic Fe3O4@COF@Ag SERS substrate combined with machine learning algorithms for detection of three quinolone antibiotics: Ciprofloxacin, norfloxacin and levofloxacin
Abstract
Quinolone antibiotics have good antibacterial properties and are commonly used antibiotics in the dairy industry. 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, Levofloxacin) 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 × 10-9M, LEV: 1.44 × 10-8M, NFX: 1.56 × 10-8M. This provides a new method for the detection of antibiotics in dairy products.
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