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Excess use of ofloxacin (OFL) and norfloxacin (NOR) in duck breeding is a significant food safety issue due to its residues. Therefore, a surface-enhanced Raman spectroscopy (SERS) method was developed to detect these analytes in duck meat to protect consumer health. The SERS conditions for OFL and NOR including the adsorption time, the volume ratio of gold nanoparticles to NaCl solution, and the volume of enhancement solution, were optimized by single factor experiments, and their values were 6 min, 3:1, and 20 μL, respectively. Furthermore, a total of 396 samples were used to establish a principal component analysis–support vector machine (PCA-SVM) model. The performance was evaluated using three pretreatment methods: adaptive iterative–penalty least squares (air-PLS) and standard normal variate (SNV), air-PLS and first derivative coupled with SNV, and air-PLS and second derivative coupled with SNV. Air-PLS and second derivative coupled with SNV was selected to be the optimal pretreatment. The sensitivity and specificity of PCA-SVM for the classification of the meat samples were 85 to 100% and 95 to 100% with an accuracy of 93%. The results show that SERS was an effective and rapid approach for the identification of OFL and NOR in duck meat.

Detection of Ofloxacin and Norfloxacin in Duck Meat Using Surface-Enhanced Raman Spectroscopy (SERS) Coupled with Multivariate Analysis