Wheat (Triticum spp.) is a widely grown cereal crop and is one of the most important staple foods internationally. Grading is an imperative step in wheat production to ensure that the grains are of acceptable quality and safety for food processing and consumption. Current wheat grading practices are performed manually which is tedious, time-consuming, and subjective. This study aimed to investigate the feasibility of NIR hyperspectral imaging (HSI) and chemometrics to discriminate sound wheat from four common defects. Defective wheat included heat-damaged, Fusarium-damaged, sprout-damaged, and immature kernels. A wide variety of pre-processing techniques and classification algorithms (logistic regression, partial least squares-discriminant analysis, linear discriminant analysis, k-nearest neighbours, decision trees, random forests and support vector machines) were evaluated for both two-way and multiclass analyses. For the two-way classifications, accuracies between 98.6% and 100% were achieved for each defective category. The multiclass analyses had a decreased performance, where the best model attained an accuracy of 84.6%. Given that agricultural products vary significantly in inherent characteristics due to genetics, cultivation practices, handling and storage, the overall results achieved are highly successful. This study shows that HSI is capable of effectively discriminating sound wheat from common occurring defects, offering a promising alternative grading technique to the cereal industry.
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