Development and pilot scale demonstration of encapsulated ferric pyrophosphate premixes for double and multiple fortified salt with vitamins B9,B12, iodine and zinc
Bibliographic record
Abstract
Ferric pyrophosphate (FePP) was evaluated as an iron fortificant alternative to ferrous fumarate due to its favorable sensory properties in fortified foods. However, its low bioavailability limits nutritional efficacy. To enhance absorption, previously developed adjuncts—disodium ethylenediaminetetraacetate dihydrate, citric acid, trisodium citrate, and sodium pyrophosphate—were incorporated, demonstrating increased FePP bioavailability in prior studies. In previous double-fortified salt (DFS) formulations, uncoated FePP in the presence of iodide caused rapid iodine loss, compromising product stability. This challenge prompted the development of a protective coating system designed to preserve iodine while maintaining iron bioavailability. Extrusion of FePP with adjuncts produced unacceptable colour changes, necessitating colour masking, similarly to approaches used with ferrous fumarate in DFS. The coating system was successfully tested on pilot scale for multi-fortified salt (MFS) formulations containing iron (FePP with adjuncts or FeFum), iodine, zinc, vitamins B 9 and B 12 . After six months of storage under elevated temperature and humidity, both DFS and MFS remained stable, confirming that soy stearin provided an effective moisture barrier and prevented significant iodine loss under typical use conditions. Zinc oxide proved superior to zinc sulfate in MFS in terms of iodine stability. Due to FePP's lower iron content and the requirement for adjuncts, higher premix quantities were necessary, increasing premix addition level and cost. However, the estimated costs of $0.86–1.05 USD per person per year, or $0.23–0.29 USD/kg of salt is still acceptable. FePP is a viable iron fortificant for salt, yielding stable, visually acceptable, and nutritionally effective DFS and MFS formulations.
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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 | 0.001 | 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".