Rapid detection and prediction of chlortetracycline and oxytetracycline in animal feed using surface-enhanced Raman spectroscopy (SERS)

Surface-enhanced Raman spectroscopy (SERS) was examined to explore the feasibility of SERS technique to develop a rapid, non-destructive, and reliable spectroscopic method for qualitative and quantitative analysis of chlortetracycline (CTC) and oxytetracycline (OTC) in animal feed. Study samples were prepared by spiking tetracycline-free animal feed at different concentration ranges. In several Raman shift regions including characteristic peaks, spectral variation and Raman intensity difference among CTC and OTC groups at different concentrations were clearly visualized, depending on the type of tetracycline. The k-nearest neighbor (KNN) and linear discriminant analysis (LDA) models yielded excellent correct classification rates while showing no or only one misclassification of spiked samples as false-negative. The first two canonical variables in the chemometric modes for classification accounted for more than 95% variation in SERS spectra. Of the models developed for predicting CTC and OTC concentrations, multiple linear regression (MLR) and partial least squares regression (PLSR) models for CTC quantification showed outstanding model performance and ability, with coefficient of determination (r2) (>0.94), low predictive error rate (<10.0 mg/kg), and acceptable linear regression slope (close to 1.0). The slightly lower predictive power and model performance were observed in MLR and PLSR models for OTC quantification. The findings and implications from this study indicate that SERS can be an alternative or supplementary analytical technique with acceptable sensitivity and accuracy to existing standard chemical methods for rapid determination of select tetracyclines in animal feeds, serving as an excellent screening system for real-time monitoring of tetracycline contaminated feed samples.