Detection of Fusarium damaged kernels in Canada Western Red Spring wheat using visible/near-infrared hyperspectral imaging and principal component analysis

Abstract

Fusarium damage in wheat reduces the quality and safety of food and feed products. In this study, the use of hyperspectral imaging was investigated to detect fusarium damaged kernels (FDK) in Canadian wheat samples. Eight hundred kernels of Canada Western Red Spring wheat were segregated into three classes of kernels: sound, mildly damaged and severely damaged. Singulated kernels were scanned with a hyperspectral imaging system in the visible-NIR (400–1000 nm) wavelength range. Principal component analysis (PCA) was performed on the images and the distribution of PCA scores within individual kernels measured to develop linear discriminant analysis (LDA) models for predicting the extent of fusarium damage. An LDA model classified the wheat kernels into sound and FDK categories with an overall accuracy of 92% or better. Classification based on six selected wavelengths was comparable to that based on the full-spectrum data.