Feasibility of surface-enhanced Raman spectroscopy for rapid detection of aflatoxins in maize.
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 μg/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 μg/kg) and quantitation (44-121 μg/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.
Office of the Texas State Chemist, Texas A&M AgriLife Research, Texas A&M University System , College Station, Texas 77841, United States.
Kyung-Min
KM
Lee
Timothy J
TJ
Herrman
Yordanos
Y
Bisrat
Seth C
SC
Murray
D000348:Aflatoxins / Q000032:analysis*; D005506:Food Contamination / Q000032:analysis*; D013059:Spectrum Analysis, Raman / Q000379:methods*; D003313:Zea mays / Q000737:chemistry*
D000348:Aflatoxins