Relative Bioavailability of Iron in Bangladeshi Traditional Meals Prepared with Iron-Fortified Lentil Dal
Bibliographic record
Abstract
Due to low Fe bioavailability and low consumption per meal, lentil must be fortified to contribute significant bioavailable Fe in the Bangladeshi diet. Moreover, since red lentil is dehulled prior to consumption, an opportunity exists at this point to fortify lentil with Fe. Thus, in the present study, lentil was Fe-fortified (using a fortificant Fe concentration of 2800 µg g−1) and used in 30 traditional Bangladeshi meals with broad differences in concentrations of iron, phytic acid (PA), and relative Fe bioavailability (RFeB%). Fortification with NaFeEDTA increased the iron concentration in lentil from 60 to 439 µg g−1 and resulted in a 79% increase in the amount of available Fe as estimated by Caco-2 cell ferritin formation. Phytic acid levels were reduced from 6.2 to 4.6 mg g−1 when fortified lentil was added, thereby reducing the PA:Fe molar ratio from 8.8 to 0.9. This effect was presumably due to dephytinization of fortified lentil during the fortification process. A significant (p ≤ 0.01) Pearson correlation was observed between Fe concentration and RFeB% and between RFeB% and PA:Fe molar ratio in meals with fortified lentil, but not for the meal with unfortified lentil. In conclusion, fortified lentil can contribute significant bioavailable Fe to populations at risk of Fe deficiency.
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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".