Quadruple fortification of salt for the delivery of iron, iodine, folic acid, and vitamin B12 to vulnerable populations
Bibliographic record
Abstract
A process for simultaneous delivery of iron, iodine, folic acid, and vitamin B12 through salt as a potential and holistic approach to ameliorate anaemia and reduce maternal and infant mortality is presented. Two approaches for adding folic acid and B12 to salt during double fortification with iron and iodine were investigated. Attempts to add both micronutrients through the iodine spray solution were unsuccessful. Hence, folic acid was added through a stabilized iodine solution, and B12 was added through the iron premix. Four approaches used to incorporate B12 into the iron premix were investigated: (1) co-extruding B12 with iron, (2) spraying B12 on the surface of the iron extrudate, (3) adding B12 to the colour masking agent, and (4) adding B12 to the outer coating. Of these approaches, coextrusion (1) was the best, based on the ease of production and stability of fortificants. The salt formulated with the solid iron-B12 premix and sprayed iodine and folic acid solution contained 1000 ppm iron, 50 ppm iodine, 25 ppm folic acid, and 0.25 ppm B12. Over 98% of B12, 93% folic acid, and 94% iodine were retained after 6-month storage in the best formulation. This technology can simultaneously deliver iron, iodine, folic acid, and vitamin B12 in a safe and stable salt enabling public health measures for improved health at a minimal additional cost.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".