Use of NMR for predicting protein concentration in soybean seeds based on oil measurements
Bibliographic record
Abstract
Abstract Increasing the level of protein in soybean seeds has been a major target for soybean [ Glycine max (L.) Merr.] breeders. The objective of this study was to examine the potential of predicting soybean seed protein based on oil values as determined by NMR. Seed protein and oil concentrations were determined in an F 2 population generated from the cross between a G. max (NK, S08‐80), and a G. soja (PI 458536) cultivar. The protein concentration in the population ranged from 40.4 to 52.6%. Protein‐oil regression analysis was used to generate an equation for predicting seed protein concentration based on oil readings. The regression equation Protein=62.3–1.3 [Oil] ( R 2 =0.46) was developed, with a corresponding correlation of −0.69 between the traits. With this equation, the mean protein concentration of the selected 25% of the population (a simulated breeding pressure) was greater than the mean of the unselected population (46.1%, SE=0.13) by about 1.9%. Individual F 2 plants that exceeded the mean protein value of the population constituted 86.4% of the selected samples. Selection based on oil concentration, however, failed to include 27.1% of the plants that were among the top 25% for protein concentration. Selection of high‐protein plants based on NMR oil measurement was reasonably effective in the test population and might offer a new and rapid method of selecting high‐protein individuals in soybean populations derived from the wild soybean progenitor, G. soja . If further tested on other populations and samples, it might be used as an analytical alternative for an indirect measurement of protein concentration based on NMR measurements of the oil.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".