DisplayTitle

Detection of Sprouted and Midge-Damaged Wheat Kernels Using Near-Infrared Hyperspectral Imaging

¹Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, Canada R3T 5V6. ²Corresponding author: Digvir_Jayas@Umanitoba.ca ³Agriculture and Agri-Food Canada, Cereal Research Centre, Winnipeg, MB, Canada R3T 2M9.

Sprout damage which results in poor breadmaking quality due to enzymatic activity of α-amylase is one of the important grading factors of wheat in Canada. Potential of near-infrared (NIR) hyperspectral imaging was investigated to detect sprouting of wheat kernels. Artificially sprouted, midge-damaged, and healthy wheat kernels were scanned using NIR hyperspectral imaging system in the range of 1000–1600 nm at 60 evenly distributed wavelengths. Multivariate image analysis (MVI) technique based on principal components analysis (PCA) was applied to reduce the dimensionality of the hyperspectral data. Three wavelengths 1101.7, 1132.2, and 1305.1 nm were identified as significant and used in analysis. Statistical discriminant classifiers (linear, quadratic, and Mahalanobis) were used to classify sprouted, midge-damaged, and healthy wheat kernels. The discriminant classifiers gave maximum accuracy of 98.3 and 100% for classifying healthy and damaged kernels, respectively.