Deoxynivalenol screening in wheat kernels using hyperspectral imaging

The use of hyperspectral imaging (HSI) for deoxynivalenol (DON) screening in wheat kernels is investigated. Experiments were carried out using a new algorithm designed to be simple to implement and computationally light, being largely based on the manipulation of a few selected spectral bands. Initial experimental results revealed that direct estimation of DON content using hyperspectral images is currently unfeasible, but they also indicated that an indirect analysis exploring the correlation between Fusarium damage and DON content may be accurate enough to improve the process of DON screening in the production chain. This motivated the adoption of a classification approach, in which an algorithm, instead of estimating a value for the DON content, classifies wheat kernel batches into two or three categories, depending on the application. The developed algorithm achieved accuracies of 72% and 81% for the three- and two-class classification schemes, respectively. The results, although not accurate enough to provide conclusive screening, indicated that the algorithm could be used for initial screening to detect wheat batches that warrant further analysis regarding their DON content.