Rapid and Reliable Qualitative Methods for Identification of Iron Fortificants in Wheat Flour (P13-015-19)
Bibliographic record
Abstract
Fortification is a common strategy to reduce the prevalence of iron deficiency anemia (IDA). Currently, manufacturers are able to fortify wheat flour with cheaper iron compounds with lower bioavailability, leading to less of an impact on IDA. Therefore, a method is needed for government agencies to monitor the type of iron added to flour. The objective was to develop a quick and simple method to qualitatively determine iron compounds commonly used for fortifying wheat flour. Unfortified wheat flour was fortified with 40 ppm using these salts: ferric pyrophosphate (FePP), ferrous sulfate (FeSO4), ferrous citrate (FeC), ferrous fumarate (FeF), and sodium iron EDTA (NaFeEDTA), except for electrolytic iron (EFe) where 60 ppm was added. Iron salts were identified based on their magnetic property, solubility in water or acid, and oxidation state. EFe was identified by passing a magnet through the flour. Ferrous and ferric salts were identified using potassium thiocyanate (KSCN) in 3 N hydrochloric acid (HCl) with and without hydrogen peroxide (H2O2). Ferric salts (NaFeDTA and FePP) were identified using Ferrozine and ascorbic acid. Poor solubility of FeF in weak acid with KSCN was used to differentiate it from FeSO4 and FeC. Acidity testing with phenolphthalein and sodium hydroxide (NaOH) further differentiated FeC from FeSO4. Flour samples were tested in triplicates and blinded samples were tested independently. EFe from flour was visible on the magnet. In addition to producing red specks with KSCN, NaFeEDTA in water produced strong color with Ferrozine and ascorbic acid, unlike FePP. Using KSCN and H2O2, FeF did not produce pink color with 0.1 N HCl, unlike FeSO4 and FeC. Acidity testing differentiated FeSO4 and FeC; FeSO4 produced pink color with less NaOH than FeC. Blinded flour samples were independently and correctly identified to confirm the validity of the methods. These quick, inexpensive, and reliable qualitative methods will be useful for agencies to identify the type of iron added to flour to monitor the quality of iron fortification strategies. Supported by Nutrition International through a grant from Global Affairs Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".