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• W4280491531 abstract "Large-scale investigations of maize kernel traits important to researchers, breeders, and processors require high throughput methods, which are presently lacking. To address this bottleneck, we developed a novel flatbed platform that automatically acquires and analyzes multiwavelength near-infrared (NIR hyperspectral) images of maize kernels precisely enough to support robust predictions of protein content, density, and endosperm vitreousness. The upward facing-camera design and the automated ability to analyze the embryo or abgerminal sides of each individual kernel in a sample with the appropriate side-specific model helped to produce a superior combination of throughput and prediction accuracy compared to other single-kernel platforms. Protein was predicted to within 0.85% (root mean square error of prediction), density to within 0.038 g/cm3, and endosperm vitreousness percentage to within 6.3%. Kernel length and width were also accurately measured so that each kernel in a rapidly scanned sample was comprehensively characterized." @default.
• W4280491531 created "2022-05-22" @default.
• W4280491531 creator A5002408039 @default.
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• W4280491531 date "2022-10-01" @default.
• W4280491531 modified "2023-10-01" @default.
• W4280491531 title "A novel high-throughput hyperspectral scanner and analytical methods for predicting maize kernel composition and physical traits" @default.
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• W4280491531 doi "https://doi.org/10.1016/j.foodchem.2022.133264" @default.
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