Iron Fortification of Lentil (Lens culinaris Medik.) to Address Iron Deficiency
Bibliographic record
Abstract
Iron (Fe) deficiency is a major human health concern in areas of the world in which diets are often Fe deficient. In the current study, we aimed to identify appropriate methods and optimal dosage for Fe fortification of lentil (Lens culinaris Medik.) dal with FeSO4·7H2O (ferrous sulphate hepta-hydrate), NaFeEDTA (ethylenediaminetetraacetic acid iron (III) sodium salt) and FeSO4·H2O (ferrous sulphate mono-hydrate). We used a colorimetric method to determine the appearance of the dal fortified with fortificants at different Fe concentrations and under different storage conditions. Relative Fe bioavailability was assessed using an in vitro cell culture bioassay. We found that NaFeEDTA was the most suitable fortificant for red lentil dal, and at 1600 ppm, NaFeEDTA provides 13–14 mg of additional Fe per 100 g of dal. Lentil dal sprayed with fortificant solutions, followed by shaking and drying at 75 °C, performed best with respect to drying time and color change. Total Fe and phytic acid concentrations differed significantly between cooked unfortified and fortified lentil, ranging from 68.7 to 238.5 ppm and 7.2 to 8.0 mg g−1, respectively. The relative Fe bioavailability of cooked fortified lentil was increased by 32.2–36.6% compared to unfortified cooked lentil. We conclude that fortification of lentil dal is effective and could provide significant health benefits to dal-consuming populations vulnerable to Fe deficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".