Iron- and Zinc-Fortified Lentil (Lens culinaris Medik.) Demonstrate Enhanced and Stable Iron Bioavailability After Storage
Bibliographic record
Abstract
Lentil ( Lens culinaris Medik.) is a quick-cooking, rapidly expanding protein-rich crop with high iron (Fe) and zinc (Zn), but low bioavailability due to the presence of phytate, similar to other grains. Lentils dual fortified with Fe and Zn can significantly improve the bioavailable Fe and Zn content. Three milled lentil product types (LPTs) were fortified with Fe using NaFeEDTA [ethylenediaminetetraacetic acid iron (III) sodium salt] (Fe fortified) or Zn from ZnSO 4 ·H 2 O (Zn fortified), or both (dual fortified). Fe, Zn, phytic acid (PA) concentration, and relative Fe bioavailability (RFeB%) were assessed for samples from two fortified batches (initial and for 1 year stored). Fe, Zn, and RFeB% increased significantly in two batches of samples from the three LPTs, and decreased by 5–15% after 1 year of storage. PA concentration decreased from 8 to 15% after fortification of all samples from two batches of the three LPTs but showed different patterns of influence after storage. Dual-fortified lentil fortified with 24 mg Fe and 12 mg Zn 100 g −1 lentil had the highest amount of Fe and Zn, and the lowest PA concentration, and RFeB% was increased from 91.3 to 519.5%. Significant ( p ≤ 0.01) Pearson correlations were observed between Fe concentration vs. PA:Fe molar ratio (MR), Fe concentration vs. RFeB%, RFeB% vs. PA:Fe MR, and Zn concentration vs. PA:Zn MR in all samples from two batches of the three LPTs. In conclusion, dual-fortified lentil can contribute significant bioavailable Fe and Zn to populations at risk of Fe and Zn 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".