Iron Fortification and Bioavailability of Chickpea (Cicer arietinum L.) Seeds and Flour
Bibliographic record
Abstract
Iron (Fe) deficiency is one of the most common nutritional disorders, and is mainly due to insufficient intake of bioavailable Fe. Chickpea (Cicer arietinum L.) was examined as a potential vehicle for Fe fortification. Fortificants (FeSO4·7H2O (ferrous sulfate hepta-hydrate), FeSO4·H2O (ferrous sulfate mono-hydrate) and NaFeEDTA (ethylenediaminetetraacetic acid iron (iii) sodium salt)) were applied by a spraying and drying method. At 2000 µg g−1 iron fortificant, the fortified split desi seeds (dal), desi flour and kabuli flour supplied 18–19 mg, 16–20 mg and 11–19 mg Fe per 100 g, respectively. The overall consumer acceptability using a nine-point hedonic scale for sensory evaluation demonstrated that NaFeEDTA-fortified cooked chickpea (soup and chapatti) scored the highest among the three fortificants. Lightness (L*), redness (a*) and yellowness (b*) of Fe-fortified products changed over time. However, no organoleptic changes occurred. Fe bioavailability was increased by 5.8–10.5, 15.3–25.0 and 4.8–9.0 ng ferritin mg−1 protein for cooked split desi seeds (soup), desi chapatti and kabuli chapatti, respectively, when prepared using Fe-fortified chickpea. Desi chapatti showed significantly higher Fe bioavailability than the other two. The increase in Fe concentration and bioavailability in fortified chickpea products demonstrated that these products could provide a significant proportion of the recommended daily Fe requirement.
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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".