Identification of wheat kernel vitreousness by hyperspectral imaging: Comparing the Visible, Vis-NIR and SWIR range

Vitreousness serves as a crucial visual indicator of grain hardness and is of paramount importance in the wheat industry due to its substantial influence on both milling and baking quality. Consequently, it is regarded as a fundamental criterion for assessing wheat quality and determining its market value. This study evaluates the efficacy of hyperspectral imaging (HSI) in classifying the grains of thirty-six wheat varieties as either vitreous or non-vitreous, focusing on classification performance across different spectral regions, including Visible (Vis), Visible-Near Infrared (Vis-NIR), and Short-Wave Infrared (SWIR). To achieve this, Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and Artificial Neural Networks (ANN) were utilised to classify grains according to their vitreousness. The results revealed that vitreous kernels were more readily classified than non-vitreous kernels, with classification accuracies of 93.01 % and 83.13 %, respectively. The highest F1 score for the test set, 85.26 %, was attained in the Vis-NIR range by SVM. Region of interest (ROI) selection improved non-vitreous classification by up to 3 %, particularly in the Vis and Vis-NIR regions. Furthermore, five critical wavelengths (540, 636, 476, 588, and 489 nm) in the Vis range were identified using the Minimum Redundancy Maximum Relevance (mRMR) approach. Notably, the reduced set of wavelengths yielded classification accuracies comparable to those obtained using the full spectrum, achieving an accuracy of 93.56 % for vitreous grains and 77.90 % for non-vitreous grains. These findings highlight the potential of HSI, particularly within the Vis region, for the non-destructive classification of wheat grain vitreousness, with colour information emerging as a vital factor in the classification process.