Prediction of sorghum oil content using near-infrared hyperspectral imaging

Background and ObjectivesAside from being a staple crop, sorghum is now being used as a gluten-free food, an animal feed ingredient, and a biofuel source. This growing demand for sorghum has increased interest in grain quality and utilization. This study explored near-infrared hyperspectral imaging (NIR HSI) as a nondestructive and rapid method to predict the oil content of sorghum grains. FindingsPartial least square (PLS) regression models for oil from NIR HSI spectra achieved 0.19% standard error of calibration (SEC) and 0.21% standard error of prediction (SEP) at 10 PLS factors. The results from the NIR HSI instrument were comparable to those from the single-kernel near-infrared reflectance instrument using the same set of samples. ConclusionThis study showed the potential of HSI as a quality control method for sorghum grains, specifically for oil content, which could be beneficial for sorghum breeders, growers, and processors. Significance and NoveltyThe increasing interest in sorghum use prompted this study which is one of the first to explore NIR HSI for sorghum oil with the ability to indicate single seed weight.