| Autor: |
Jose I, Varela, Nathan D, Miller, Valentina, Infante, Shawn M, Kaeppler, Natalia, de Leon, Edgar P, Spalding |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Food Chemistry. 391:133264 |
| ISSN: |
0308-8146 |
| Popis: |
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/cm |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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