Quantification and classification of deoxynivalenol-contaminated oat samples by near-infrared hyperspectral imaging.

Abstract

Deoxynivalenol (DON) is the most occurring mycotoxin in oat and oat-based products. Near-infrared hyperspectral imaging (NIR-HSI) has been proposed as a promising methodology for analysing DON contamination in the food industry. The present study aims to apply NIR-HSI for DON detection in oat kernels and to quantify and classify naturally DON-contaminated oat samples. Unground and ground oat samples were scanned by NIR-HSI before their DON content was determined by HPLC. The data were pre-treated and analysed by PLS regression and four classification methods. The most efficient DON prediction model was for unground samples (R2=0.75 and RMSEP=403.18μg/kg), using twelve characteristic wavelengths with a special interest in 1203 and 1388nm. The random forest algorithm of unground samples according to the EU maximum limit for unprocessed oats (1750μg/kg) achieved a classification accuracy of 77.8 %. These findings indicate that NIR-HSI is a promising tool for detecting DON in oats.