Influence of grain topography on near infrared hyperspectral images

Abstract

Near infrared hyperspectral imaging (NIR-HSI) allows spatially resolved spectral information to be collected without sample destruction. Although NIR-HSI is suitable for a broad range of samples, sizes and shapes, topography of a sample affects the quality of near infrared (NIR) measurements. Single whole kernels of three cereals (barley, wheat and sorghum), with varying topographic complexity, were examined using NIR-HSI. The influence of topography (sample shape and texture) on spectral variation was examined using principal component analysis (PCA) and classification gradients. The greatest source of variation for all three grain types, despite spectral preprocessing with standard normal variate (SNV) transformation, was kernel curvature. Only 1.29% (PC5), 0.59% (PC6) and 1.36% (PC5) of the spectral variation within the respective barley, wheat and sorghum image datasets was explained within the principal component (PC) associated with the chemical change of interest (loss of kernel viability). The prior PCs explained an accumulated total of 91.18%, 89.43% and 84.39% of spectral variance, and all were influenced by kernel topography. Variation in sample shape and texture relative to the chemical change of interest is an important consideration prior to the analysis of NIR-HSI data for non-flat objects.