Optimization of the color masking and coating unit operations for microencapsulating ferrous fumarate for double fortification of salt
Bibliographic record
Abstract
Abstract A new coating formulation was developed to eliminate the factor that caused black spots on the iron premix surface, used for making Double Fortified Salt. The formulation is a suspension of titanium dioxide in soy stearin, prepared with ethanol and dichloromethane and applied with a glass sprayer and pan coater. 0–20%w/wtitanium dioxide was suspended in 10%w/wsoy stearin/hydroxypropyl methylcellulose. Coating with a suspension of 15%w/wTiO2in 10%w/wsoy stearin ensured that all the TiO2adheres to the premix surface, giving no chance for the recycling of iron contaminated TiO2, which caused the black spot. The new coating formulation ensured that over 90% iodine in Double Fortified Salt was retained after 6 months at 45 °C, 60–70% RH. The whiteness of the premix (L* = 86.4) matched the Double Fortified Salt whiteness (L* = 86.8). Thus, making the new coating method as effective as the previous in desirable characteristics. More so, the new coating method simplifies the existing method by merging the previous color masking, and double coating steps into one step.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".