Dual-Fortified Lentil Products—A Sustainable New Approach to Provide Additional Bioavailable Iron and Zinc in Humans
Bibliographic record
Abstract
Background Iron (Fe) and zinc (Zn) deficiencies are global health problems affecting 20% and 33% of the world's population, respectively. Lentil ( Lens culinaris Medik.), part of the staple food supply in many countries, can be a potential vehicle for Fe and Zn fortification. Objective We developed a dual-fortification protocol to fortify 3 milled lentil product types (LPTs) [red-football (RF), red-split (RS), and yellow-split (YS)], with NaFeEDTA and ZnSO 4 .H 2 O to increase the bioavailable content of Fe and Zn. Methods Appropriate Fe and Zn doses were determined to fortify lentils based on RDAs. Relative Fe bioavailability (RFeB%) and phytic acid (PA) content were assessed using an in vitro Caco-2 cell bioassay and PA analysis, respectively. One-factor ANOVA determined the differences in colorimetric score; concentrations of Fe, Zn, and PA; and RFeB% among samples. The least significant difference was calculated with significance level set at P < 0.05. Results Fe and Zn concentration and RFeB% increased and PA concentration decreased significantly in dual-fortified lentils. Dual-fortified lentil samples had higher RFeB% compared with Fe-fortified (single) samples in all 3 LPTs, whereas RFeB% decreased in Zn-fortified (single) RF and YS samples by 43.4% and 36%, respectively. The RF, RS, and YS samples, fortified with 16 mg Fe and 8 mg Zn/100 g of lentils, provided 27 mg Fe and 14 mg Zn, 28 mg Fe and 13.4 mg Zn, and 29.9 mg Fe and 12.1 mg Zn, respectively. RFeB% of RF, RS, and YS lentil samples increased by 91–307%, 114–522%, and 122–520%, respectively. Again, PA concentrations of RF, RS, and YS lentils were reduced by 0.63–0.53, 0.83–0.71, and 0.96–0.79 mg/g, respectively. Conclusions Dual-fortified lentil consumption can cost-effectively provide a significant part of the daily bioavailable Fe and Zn requirements of people with these 2 globally important micronutrient deficiencies.
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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 | 0.001 | 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".