preprints_ui: 7j9cy_v1
Data license: ODbL (database) & original licenses (content) · Data source: Open Science Framework
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| 7j9cy_v1 | Protein, weight, and oil prediction by single-seed near-infrared spectroscopy for selection of seed quality and yield traits in pea (Pisum sativum) | Background: Pea (Pisum sativum) is a prevalent cool season crop that produces seeds valued for high protein content. Modern cultivars have incorporated several traits that improved harvested yield. However, progress toward improving seed quality has received less emphasis, in part due to the lack of tools for easily and rapidly measuring seed traits. In this study we evaluated the accuracy of single-seed near-infrared spectroscopy (NIRS) for measuring pea seed weight, protein, and oil content. A total of 96 diverse pea accessions were analyzed using both single-seed NIRS and wet chemistry methods. To demonstrate field relevance, the single-seed NIRS protein prediction model was used to determine the impact of seed treatments and foliar fungicides on protein content of harvested dry peas in a field trial. Results: External validation of Partial Least Squares (PLS) regression models showed high prediction accuracy for protein and weight (R2 = 0.94 for both) and less accuracy for oil (R2 = 0.75). Single seed weight was not significantly correlated with protein or oil content in contrast to previous reports. In the field study, the single-seed NIRS predicted protein values were within 1% of an independent analytical reference measurement and were sufficiently precise to detect small treatment effects. Conclusion: The high accuracy of protein and weight estimation show that single-seed NIRS could be used in the dual selection of high protein, high weight peas early in the breeding cycle allowing for faster genetic advancement toward improved pea nutritional quality. | 2020-05-03T21:10:33.620933 | 2020-08-25T16:07:00.868877 | 2020-05-04T05:34:07.497457 | 2020-03-22T04:00:00 | https://doi.org/10.1002/jsfa.10389 | agrixiv | 1 | accepted | 1 | 1 | https://doi.org/10.31220/osf.io/7j9cy | CC-By Attribution 4.0 International | High-throughput phenotyping; Pisum sativum; near-infrared spectroscopy; nutritional quality; seed protein; single-seed phenotyping | ["High-throughput phenotyping", "Pisum sativum", "near-infrared spectroscopy", "nutritional quality", "seed protein", "single-seed phenotyping"] | Gokhan Hacisalihoglu; Jelani Freeman; Paul Armstrong; Brad Seabourn; Lyndon Porter; A. Mark Settles; Jeff Gustin | [{"id": "t79py", "name": "Gokhan Hacisalihoglu", "index": 0, "orcid": null, "bibliographic": true}, {"id": "7kwfq", "name": "Jelani Freeman", "index": 1, "orcid": null, "bibliographic": true}, {"id": "wjhfd", "name": "Paul Armstrong", "index": 2, "orcid": null, "bibliographic": true}, {"id": "cwu6q", "name": "Brad Seabourn", "index": 3, "orcid": null, "bibliographic": true}, {"id": "cg9xa", "name": "Lyndon Porter", "index": 4, "orcid": null, "bibliographic": true}, {"id": "4dxme", "name": "A. Mark Settles", "index": 5, "orcid": null, "bibliographic": true}, {"id": "qg3my", "name": "Jeff Gustin", "index": 6, "orcid": "0000-0002-5913-0200", "bibliographic": true}] | Gokhan Hacisalihoglu | Life Sciences; Plant Sciences; Plant Biology; Agriculture; Agricultural Science; Plant Breeding and Genetics Life Sciences | [{"id": "59bac99954be810318151fff", "text": "Life Sciences"}, {"id": "59bac99954be81031815200a", "text": "Plant Sciences"}, {"id": "59bac99a54be810318152028", "text": "Plant Biology"}, {"id": "59bac99b54be810318152038", "text": "Agriculture"}, {"id": "59bac99b54be81031815203a", "text": "Agricultural Science"}, {"id": "59bac9a154be810318152144", "text": "Plant Breeding and Genetics Life Sciences"}] | https://osf.io/download/5eaf334f69d3e100a8dedd39 | 0 | null | 2025-04-09T20:03:57.381147 |