Influence of grain topography on near infrared hyperspectral images.

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.

Characterisation of non-viable whole barley, wheat and sorghum grains using near-infrared hyperspectral data and chemometrics.

Undesired germination of cereal grains diminishes process utility and economic return. Pre-germination, the term used to describe untimely germination, leads to reduced viability of a grain sample. Accurate and rapid identification of non-viable grain is necessary to reduce losses associated with pre-germination. Viability of barley, wheat and sorghum grains was investigated with near-infrared hyperspectral imaging. Principal component analyses applied to cleaned hyperspectral images were able to differentiate between viable and non-viable classes in principal component (PC) five for barley and sorghum and in PC6 for wheat. An OH stretching and deformation combination mode (1,920-1,940 nm) featured in the loading line plots of these PCs; this water-based vibrational mode was a major contributor to the viable/non-viable differentiation. Viable and non-viable classes for partial least squares-discriminant analysis (PLS-DA) were assigned from PC scores that correlated with incubation time. The PLS-DA predictions of the viable proportion correlated well with the viable proportion observed using the tetrazolium test. Partial least squares regression analysis could not be used as a source of contrast in the hyperspectral images due to sampling issues.