Predicting the Stability of Double Fortified Salt by Determining the Coating Quality of the Encapsulated Iron Premix
Bibliographic record
Abstract
The technology to simultaneously fortify salt with iron and iodine was developed in Canada and transferred and scaled up in India. The double fortified salt has reached more than 60 million consumers so far. Double fortification of salt is a cost-effective and reliable means of improving iron and iodine deficiencies at a population level. However, high-quality iron premix is essential for the stability of iodine and the program’s success. Therefore, we developed a reliable and cost-effective method for premix coating quality evaluation in the field, especially in low-income settings. The integrity and chemical composition of the coating and exposure of iron at the surface (∼10 μm deep) were determined using scanning electron microscopy and energy-dispersive X-ray spectroscopy to predict the stability of the fortified salt. The phenanthroline colour dropper test was used to test the quality of the double fortified salt by reaction with ferrous iron present on the premix surface. Five iron premix samples were compared. Based on the iron release, coating composition, and the reaction with phenanthroline, Premix-3, and its corresponding DFS, obtained from a local shop in India had the lowest quality among all samples tested. The results of the dropper test corresponded with the analysis using sophisticated analytical tools, confirming it as a simple, reliable, and cost-effective test for iron premix coating quality and integrity. This simple test would be crucial for a successful double fortification program, especially in low-income countries, in predicting iron premix quality, a critical determinant of iodine stability during storage, distribution, and retail. These study results can help governments and NGOs to establish quality standards for iron premix used for salt fortification programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".