Iron Bioavailability in Low Phytate Pea
Bibliographic record
Abstract
ABSTRACT Field pea (Pisum sativum L.) seeds have high nutritional value but also contain potential antinutritional factors, such as phytate and polyphenols. Phytate can store up to 80% of the phosphorus in seeds. In the seed and during digestion it can complex minerals such as iron and zinc and make them unavailable for absorption. Also, it is not well digested by monogastrics. Polyphenols are known to reduce bioavailability of some nutrients. The objective of this research was to evaluate the effects of phytate and seed coat polyphenols on bioavailability of iron from field pea seeds. To increase the nutritional value of field pea seeds, two low‐phytate lines (1–150–81 and 1–2347–144) containing higher inorganic phosphorus concentration (IN‐P) and lower phytate‐phosphorus concentration (PA‐P) than the normal phytate varieties were developed from the cultivar CDC Bronco in previous research. Total iron concentration (FECON) did not differ significantly between normal and low phytate varieties. However, iron bioavailability (FEBIO) of the two low‐phytate lines was 1.4 to 1.9 times higher than that of the three normal phytate varieties as assessed using a cell culture bioassay. Environment also had a significant effect on FEBIO. Peas with pigmented seed coats had seven times lower FEBIO than peas with nonpigmented seed coats. Removal of the seed coat increased FEBIO in peas with pigmented seed coat five to six times.
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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".