Abstract

Estrogen residues, including diethylstilbestrol in chicken, are one of the main food safety concerns all over the world owing to a series of negative effects on the human body. Surface-enhanced Raman spectroscopy (SERS) coupled with multivariate analysis was applied to detect rapidly diethylstilbestrol residues in chicken. The detection conditions, including the sizes of colloidal gold nanoparticles (Au NPs) and the additional amounts of Au NPs, chicken extract containing diethylstilbestrol, and magnesium sulfate solution, as well as the adsorption time, were optimized by a single factor experiment to obtain a better detection effect of diethylstilbestrol residues in chicken. Partial least squares regression (PLSR) was the best quantitative model for the detection of diethylstilbestrol residues in chicken by comparing four chemometric models. Diethylstilbestrol residues in chicken could be predicted by PLSR with the low root mean square error (RMSE = 0.4128 mg/L), and the high determination coefficient (R² = 0.9811) and ratio of prediction to deviation (RPD = 7.2566) for the test set. A novel approach, which has the potential for the analysis of other estrogen residues in meat, was developed to detect rapidly the diethylstilbestrol residues in chicken by using SERS coupled with multivariate analysis.

© 2018 The Author(s)

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