Optimization of Unit Operations for Microencapsulating Ferrous Fumarate During Scale-Up of Double Fortification of Salt with Iron and Iodine
Bibliographic record
Abstract
Abstract Objectives This study evaluates factors responsible for the floating of iron premix in double fortified salt (DFS), which initially affected the large-scale implementation of the salt fortification program in India, and provides solutions to the scale-up of the technology. Materials and Methods To mitigate this time-sensitive scale-up challenge. First, the iron premix samples were obtained from the industrial scale-up pilot studies in India, evaluated for the impact of the amount of coating material (5 per cent, 7.5 per cent, and 10 per cent (in weight)), type of formulation (soy stearin, SEPIFILM and hydroxypropyl methylcellulose), amount of titanium dioxide (25-35 per cent (in weight)) used for color masking; Second, we studied the effect of change in the composition of the coating, from 10 per cent (in weight) soy stearin to a double coat with 5 per cent (in weight) hydroxypropyl methylcellulose and 5 per cent soy stearin or 10 per cent soy stearin and 1 per cent (in weight) lecithin mixture, on particle density, floating or sinking property of the iron premix, and on the stability of iodine in the DFS. Results It was observed that the hydrophobic nature and the amount of soy stearin used for coating caused the floating issue. The double coating with 5 per cent hydroxypropyl methylcellulose and 5 per cent soy stearin was preferred because lecithin in soy stearin enhanced the moisture-aided adverse interaction between iron and iodine. Shelf-life storage studies proved over 80 per cent iodine retention after 12 months of storage in the DFS formulated with iron premix double-coated with hydroxypropyl methylcellulose and soy stearin. Conclusion This proffered solution enabled the full implementation of the double fortification program in India.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".