Prediction of sorghum oil content using near‐infrared hyperspectral imaging

Abstract

BACKGROUND AND OBJECTIVES: Aside 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. FINDINGS: Partial 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. CONCLUSION: This 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 NOVELTY: The 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.

Plant phenotyping relevance

ソルガム種子の油含量という植物器官形質を、NIRハイパースペクトル画像から非破壊推定する手法の開発・精度評価が研究の中心である。

Code and data availability

The article describes NIR hyperspectral imaging of 76 sorghum samples and PLS modeling, but contains no data availability statement, no public dataset or image deposit, and no author code/model release. The only URLs are the article DOI, ORCID profiles, and cited external references (USDA/EPA/USGC reports), none of the

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