GA-SVR Optimized Surface-Enhanced Raman Spectroscopy for Rapid Detection of Ciprofloxacin Residues in Chicken Blood

Ciprofloxacin residues in chicken blood pose a potential food safety risk; however, rapid detection methods for complex chicken blood matrices are lacking. This study aimed to establish a surface-enhanced Raman spectroscopy (SERS) method for the rapid detection of ciprofloxacin in chicken blood using gold colloid as the SERS substrate. Gold colloid was synthesized via the Frens method with slight modification, and key SERS detection conditions were systematically optimized to maximize SERS intensities at 1265 cm^-1, including the amount of trisodium citrate solution, the electrolyte type, the amount of gold colloid, the amount of NaCl solution, and the adsorption time. Raw SERS spectra were pretreated with adaptive iteratively reweighted penalized least squares (air-PLS) combined with Savitzky-Golay (SG) smoothing. A genetic algorithm (GA) was used to extract characteristic Raman shifts, and a GA-SVR prediction model with radial basis function (RBF) as the kernel was constructed, with its performance compared with multivariate linear regression (MLR) and partial least squares regression (PLSR) models. The GA-SVR model exhibited the best performance, with a coefficient of determination for the calibration set (Rc2) value of 0.9893 and for the prediction set (Rp2) value of 0.9874. The root mean square error of calibration (RMSEC) and prediction (RMSEP) were 1.2953 and 1.8617, respectively, outperforming the MLR and PLSR models. These results demonstrate that the SERS method combined with GA-SVR enables rapid quantitative detection of ciprofloxacin residues in chicken blood, providing a technical reference for monitoring veterinary drug residues in livestock and poultry products.