Differences in Relative Iron Bioavailability in Traditional Bangladeshi Meal Plans
Bibliographic record
Abstract
BACKGROUND: Iron deficiency is the most common nutrient deficiency worldwide. Large intakes of micronutrient-poor staple crops, coupled with low intakes of highly bioavailable dietary iron, are a major cause of this deficiency. OBJECTIVE: To examine the concentration and relative bioavailability of iron in several models (n = 23) of traditional Bangladeshi meals (rice, lentils/dal, vegetable, and fish), as well as the effect of removal of the lentil seed coat on the nutritional quality of iron. METHODS: The relative bioavailability of iron was assessed by the in vitro/Caco-2 cell culture method, iron concentration by an inductively coupled argon-plasma emission spectrometer (ICAP-ES), and phytic acid concentration by colorimetric assay. The recipes contained 75% to 85% rice, 0% to 15% dal (containing whole or dehulled lentils), 0% to 15% vegetable curry, and 0% to 8% fish. RESULTS: While the iron concentrations of recipes containing dehulled dal were significantly lower than those of recipes containing whole dal (p = .005), seed coat removal doubled relative iron bioavailability and increased phytic acid concentration (p < .001). The addition of fish to the meals had no significant effect on relative iron bioavailability. Iron concentration and relative iron bioavailability were correlated in the recipes containing dehulled dal (r = 0.48, p = .03), but not whole dal (r = -0.047, p = .84). CONCLUSIONS: The total amount of iron absorbed from traditional Bangladeshi meals is dependent upon iron concentration, and dehulling lentils removes inhibitory factors increasing iron uptake but also increases the density of phytic acid in the lentil sample. Thus, along with breeding for high iron concentration and bioavailability (i.e., biofortification), seed coat removal plus measures to lower phytic acid concentrations may be an important strategy to improve the bioavailability of iron in lentils and other pulse crops.
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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".