MétaCan
Menu
Back to cohort
Record W2950519650 · doi:10.1093/cdn/nzz036.p13-015-19

Rapid and Reliable Qualitative Methods for Identification of Iron Fortificants in Wheat Flour (P13-015-19)

2019· article· en· W2950519650 on OpenAlexaffabout
Nicole Hanson, Isaac Agbemafle, Manpreet Chadha, Manju Reddy

Bibliographic record

VenueCurrent Developments in Nutrition · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsNutrition International
Fundersnot available
KeywordsIdentification (biology)Wheat flourFood scienceBiologyBotany

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.082
GPT teacher head0.397
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueCurrent Developments in NutritionSame topicPlant Micronutrient Interactions and EffectsFrench-language works237,207