Iron Bioavailability in Field Pea Seeds: Correlations with Iron, Phytate, and Carotenoids
Bibliographic record
Abstract
Field pea (Pisum sativum L.) is a nutritious pulse crop consumed as food and animal feed in many countries. The present study was performed to determine the potential effects of Fe, phytate, and carotenoid concentrations on Fe bioavailability (FeBIO) of field pea seeds. Selected PR‐07 (recombinant inbred line [RIL] population derived from the cross ‘Carrera’/‘CDC Striker’) lines, which segregated for Fe concentration and cotyledon color, showed positive correlation between their Fe concentration and FeBIO. In a second study, lines 4802‐8 (derived from the cross 1‐2347‐144/‘CDC Raezer’) and 4803‐4 (derived from the cross 1‐150‐81/‘CDC Limerick’), segregating for phytate concentration and cotyledon color were evaluated for FeBIO. Phytate concentration was negatively correlated with FeBIO in 4802‐8 (r = −0.34) and 4803‐4 (r = −0.37) sublines. Four carotenoid compounds (lutein, violaxanthin, zeaxanthin, and β‐carotene) were measured in seeds of 4802‐8 and 4803‐4 sublines and summed to determine total carotenoid concentration. Green cotyledon and yellow cotyledon pea sublines did not differ significantly in total carotenoid concentration; β‐carotene was detected in green cotyledon sublines but not in yellow cotyledon sublines. Although no significant correlation was detected between total carotenoid concentration and FeBIO, in 4802‐8 sublines lutein concentration was positively correlated (r = 0.41) with FeBIO. This research shows the potential positive associations between low phytate, high Fe, and high carotenoid concentration with improved FeBIO in pea seeds to improve Fe nutrition of foods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".