Abstract
The surface-enhanced activities of size- and shape-controlled gold nanoparticles (AuNPs) with superior chemical stability were investigated to explore a possible development of a simple and non-destructive spectroscopic method to help the regulatory agency's analytical services for rapid detection and characterization of selected antimicrobials in animal feeds. Feed samples spiked at different concentration ranges of antimicrobials were evaluated using AuNPs as a surface-enhanced Raman spectroscopy (SERS) agent. The collected SERS spectra were mathematically preprocessed for further analysis. The classification models obtained 100% predictive accuracy with zero or little misclassification. The first two canonical variables (p = 0.001) could explain >95% of the variability in preprocessed spectral data. Most chemometric models for predicting MON, DEC, and LAS concentrations showed a high predictive accuracy (r2 > 0.90), lower predictive error (<20 mg/kg), and satisfactory regression quality (slope close to 1.0). The statistical results showed no statistically significant difference between the reference and SERS predicted values (p > 0.05). The findings and implications from the study indicate that SERS would be a powerful and efficient technique possessing a great potential serving as an excellent monitoring and screening tool for antimicrobial contaminated samples in the on-site analysis.
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