Rapid and non-invasive analysis of deoxynivalenol in durum and common wheat by Fourier-Transform Near Infrared (FT-NIR) spectroscopy

Fourier transform near-infrared spectroscopy (FT-NIR) was used for rapid and non-invasive analysis of deoxynivalenol (DON) in durum and common wheat. The relevance of using ground wheat samples with a homogeneous particle size distribution to minimize measurement variations and avoid DON segregation among particles of different sizes was established. Calibration models for durum wheat, common wheat and durum + common wheat samples, with particle size <500 mu m, were obtained by using partial least squares (PLS) regression with an external validation technique. Values of root mean square error of prediction (RMSEP, 306-379 mu g kg(-1)) were comparable and not too far from values of root mean square error of cross-validation (RMSECV, 470-555 mu g kg(-1)). Coefficients of determination (r(2)) indicated an "approximate to good" level of prediction of the DON content by FT-NIR spectroscopy in the PLS calibration models (r(2) = 0.71-0.83), and a "good" discrimination between low and high DON contents in the PLS validation models (r(2) = 0.58-0.63). A "limited to good" practical utility of the models was ascertained by range error ratio (RER) values higher than 6. A qualitative model, based on 197 calibration samples, was developed to discriminate between blank and naturally contaminated wheat samples by setting a cut-off at 300 mu g kg(-1) DON to seperate the two classes. The model correctly classified 69% of the 65 validation samples with most misclassified samples (16 of 20) is suitable for the determination of DON in unprocessed wheat at levels far below the maximum permitted limits set by the European Commission.