Detection of Broken Kernels Content in Bulk Wheat Samples Using Near-Infrared Hyperspectral Imaging

Abstract

Broken kernels in stored wheat are contaminants, and these reduce the quality of wheat and increase risk of spoilage. The present study was conducted to predict the amount of broken kernels in bulk Canada Western Red Spring wheat samples using a near-infrared hyperspectral imaging system. The hyperspectral images of bulk wheat samples with different levels of broken kernels (0, 3, 6, 9, 12, and 15 %) were acquired in the 1000–1600 nm wavelength range at a 10-nm interval. The reflectance spectra acquired from these samples were used to develop regression models using principal component regression (PCR) and partial least-squares regression (PLSR) techniques. A tenfold cross-validation technique was used for determining the optimal number of components required to develop these regression models. Among the two regression techniques used, the PLSR technique [with mean square errors of predictions (MSEP), standard error of cross-validation (SECV), and correlation coefficient of 0.483, 0.70, and 0.94, respectively] performed better than the PCR technique (with MSEP, SECV, and correlation coefficient of 0.74, 0.86, and 0.88, respectively). The classification models were developed using the linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) statistical techniques. Both classifiers accurately classified (100.0 ± 0.0 %) the uncontaminated wheat sample from the higher contamination level (>6 %) wheat samples. Both classifiers also gave similar classification results (QDA 89.8 ± 2.6 % and LDA 87.7 ± 1.6 %) when multi-way classification was performed to classify uncontaminated sample from the contaminated samples.