Sensory evaluations of a novel iron and zinc‐enriched powder for the potential treatment and prevention of iron deficiency in women of reproductive age
Bibliographic record
Abstract
Iron deficiency (ID) and ID with anaemia (IDA) are serious global health problems that disproportionately affect women aged 15-49 years. Although food fortification is one of the most effective and sustainable ways to combat nutritional deficiencies, iron remains one of the most difficult micronutrients to fortify, given its tendency to react strongly with food constituents. Therefore, it is important to assess the sensory properties of foods fortified with iron to determine the acceptability and palatability in target populations. We aimed to determine the palatability and acceptability of a novel iron and zinc-enriched powder fortified in tap water by conducting sensory evaluations in 35 women of reproductive age using a 9-point hedonic scale, where participants rated the sensory properties of six samples containing different amounts of the active or placebo powder. We found significant differences between samples reconstituted at 1, 2, and 3 g/L for sensory properties, including overall taste. Participants were found to be more willing to drink the mineral-enriched powder when prepared at the lowest concentration (1 g/L) compared to higher concentrations. Our results provide important insight into the sensory qualities of a novel formulation of an iron and zinc-enriched powder for at-home fortification and indicate consumer acceptability in reproductive-aged women, a key group at risk for ID/IDA. If found to improve iron status, novel treatments like this product will contribute to global efforts to develop safe, acceptable and sustainable interventions for ID and IDA.
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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 | 0.000 | 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) | 0.003 | 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".