Production of Iron Premix for the Fortification of Table Salt
Bibliographic record
Abstract
Abstract The feasibility of using fluidized bed technology for the production of an iron premix of ferrous fumarate encapsulated with soy stearine for iron fortification of table salt has been successfully demonstrated. Ferrous fumarate was selected as source of iron, in preference to ferrous sulphate heptahydrate, ferric sodium EDTA and reduced elemental iron, for its bioavailability, bland taste, stability and high iron content. Potassium iodate was used as iodine source. Fluid-bed processors of 7, 200 and 500 kg capacity were used to granulate ferrous fumarate with a solution containing, hydroxypropylmethylcellulose, sodium hexametaphosphate and titanium dioxide for binding, stabilization and colour masking, respectively. Encapsulation of granulated ferrous fumarate was achieved by top-spraying hot soy stearine containing more titanium dioxide at 98ºC on the fluidized bed. The premix contained 46.4 wt. % of ferrous fumarate. Double Fortified Salt (DFS), made by dry blending 1 part of iron premix and 150 parts of already iodized salt, contained 1000 ppm of iron and 50 ppm of iodine. Less than 10% of the iron in the premix, with a bulk density of 0.78 g/cc, dissolved in HCl at pH of 4.0 indicating good coating integrity. Particle size distribution of iron premix was consistently centred on 300 µm with more than 90% of the particles in the size range of 150 to 710 µm, similar to that of free flowing refined salt.
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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 | 0.000 | 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".