Rapid and Reliable Method for Qualitative and Quantitative Assessment of Iron Fortificants Used for Flour Fortification
Bibliographic record
Abstract
Food fortification with iron has potential to reduce anemia if the manufacturers comply with fortification standards using highly bioavailable and required quantities of iron. Our objective was to develop a quick and simple method to identify and quantify iron compounds commonly used for flour fortification to help agencies monitor fortification programs. Wheat and corn flours were fortified with 40-60 mg Fe/kg using ferric pyrophosphate, ferrous sulfate, ferrous citrate, ferrous fumarate, sodium ferric EDTA, and electrolytic iron. Using potassium thiocyanate with hydrochloric acid and hydrogen peroxide, we identified EFe, ferric, and ferrous fortificants. Ferric and ferrous salts were differentiated based on their solubility in water using ferrozine with and without added ascorbic acid. Semi-quantification by visual analysis was not significantly different from two standard quantitative methods. This study provides practical options to identify and quantify Fe fortificants based on the resources available at the facilities in various countries. Correct identification of unknown iron samples by independent personnel and accuracy of visual quantification suggested the reliability, ease, and accuracy of these methods in identifying and quantifying iron fortificants.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".