Feasibility of surface-enhanced Raman spectroscopy for rapid detection of aflatoxins in maize.
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Rapid and sensitive surface-enhanced Raman spectroscopy (SERS) for aflatoxin detection was employed for development of the models to classify and quantify aflatoxin levels in maize at concentrations of 0 to 1,206 g/kg. Highly effective SERS substrate (Ag nanosphere) was prepared and mixed with a sample extract for SERS measurement. Strong Raman bands associated with aflatoxins and changes in maize kernels induced by aflatoxin contamination were observed in different SERS spectroscopic regions. The k-nearest neighbors (KNN) classification model yielded high classification accuracy and lower prediction error with no misclassification of contaminated samples as aflatoxin negative. The multiple linear regression (MLR) models showed a higher predictive accuracy with stronger correlation coefficients (r = 0.939-0.967) and a higher sensitivity with lower limits of detection (13-36 g/kg) and quantitation (44-121 g/kg) over other quantification models. Paired sample t test exhibited no statistically significant difference between the reference values and the predicted values of SERS in most chemometric models. The proposed SERS method would be a more effective and efficient analytical tool with a higher accuracy and lower constraints for aflatoxin analysis in maize compared to other existing spectroscopic methods and conventional Raman spectroscopy.
author list (cited authors)
Lee, K., Herrman, T. J., Bisrat, Y., & Murray, S. C.
complete list of authors
Lee, Kyung-Min||Herrman, Timothy J||Bisrat, Yordanos||Murray, Seth C