Journal
FOOD CHEMISTRY
Volume 391, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2022.133264
Keywords
Hyperspectral imaging; Maize composition; Vitreousness; NIR; PLSR; PLS-DA
Funding
- United States Department of Agriculture (USDA) [WIS03049]
- National Science Foundation [1940115]
- Direct For Biological Sciences
- Division Of Integrative Organismal Systems [1940115] Funding Source: National Science Foundation
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Researchers have developed a flatbed platform that can efficiently acquire and analyze the traits of maize kernels, accurately predicting protein content, density, and endosperm vitreousness.
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.
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