Vitamin A Stability in Salt Triple Fortified with Iodine, Iron, and Vitamin A
Bibliographic record
Abstract
BACKGROUND: Dietary micronutrient deficiencies, which lead to diseases such as iodine deficiency disorders, iron-deficiency anemia, and vitamin A deficiency, are serious public health problems in the developing world. Fortifying salt with iodine, iron, and vitamin A is an attractive approach to simultaneously reduce the deficiencies of these three micronutrients in the diet. OBJECTIVE: To explore the technical feasibility of producing triple-fortified salt fortified with iodine, iron, and vitamin A that would be stable under the climatic conditions of developing countries (i.e., high temperature and high humidity). METHODS: Triple-fortified salt was obtained by granulation and encapsulation of commercially produced vitamin A products, iodine, and iron compounds. Vitamin A retention was determined in the presence of five iron and two iodine compounds, in different combinations, under three different storage conditions. The influence of commercial stabilization techniques for the vitamin A palmitate source used (spray-dried or dissolved in oil), and the type of binder used for granulation on vitamin A retention in triple-fortified salt was studied. The influence of temperature, humidity, and chemical interactions on vitamin A stability in triple-fortified salt was also investigated. RESULTS: The most stable formulation retained 77.73% of vitamin A after 2 months of storage at 40 degrees C, 60% relative humidity, and 95% under ambient conditions. CONCLUSIONS: The results indicate that the production of a stable triple-fortified salt is technically feasible.
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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".