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Application of Raman spectroscopy for qualitative and quantitative analysis of aflatoxins in ground maize samples

Lee, Kyung-Min, Herrman, Timothy J., Yun, Unil
Journal of cereal science 2014 v.59 no.1 pp. 70-78
Raman spectroscopy, aflatoxins, breeding, chemometrics, corn, discriminant analysis, high performance liquid chromatography, hybrids, least squares, models, normal values, prediction, quantitative analysis
The applicability of Raman spectroscopy combined with chemometrics using different preprocessed spectra data was examined to develop fast, low-cost, and non-destructive spectroscopic methods for classification and quantification of aflatoxin-contaminated maize samples within the aflatoxin concentration range of 0–1206 μg/kg. This technique will find useful application in evaluating large numbers (e.g. >2000) of samples from maize hybrid performance trials and breeding programs. The best discriminant models were obtained from the linear discriminant analysis (LDA). The LDA models on validation samples showed correct classification rates in the range of 94–100% and did not misclassify any aflatoxin contaminated samples as aflatoxin negative. Of the models for predicting aflatoxin concentration, the partial least squares regression (PLSR) models showed the best quality of regression (slopes of 0.939–0.990) and highest coefficient of determination (r2 = 0.941–0.957). The models provide limited applicability to quantify aflatoxin concentration below 20 μg/kg. No significant difference was observed between predicted values using Raman spectroscopy and reference values using high-performance liquid chromatography (HPLC) (p > 0.05), indicating the suitability of Raman spectroscopy to rapidly screen large numbers of maize samples for aflatoxin contamination.