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Record W4308834419 · doi:10.1080/19440049.2022.2141468

Occurrence of glycidyl esters in infant formula products on the Canadian market between 2015 and 2019

2022· article· en· W4308834419 on OpenAlexaffabout
J.F. Schneider, Adam Becalski, Tony S. N. Zhao, Yihui Liu, Fuqi Chen, Dorothea F.K. Rawn

Bibliographic record

VenueFood Additives & Contaminants Part A · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsDepartment of National DefenceHealth Canada
Fundersnot available
KeywordsGlycidolInfant formulaIsotope dilutionChemistryDilutionChromatographyFood scienceOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Glycidyl fatty acid esters (GEs) are processing contaminants formed during refining steps of vegetable oils. 'In vivo' hydrolysis of GEs releases potentially carcinogenic and genotoxic glycidol (2,3-epoxy-1-propanol). Occurrence of GEs in vegetable oils used for infant formula manufacturing may pose a potential health concern for formula-fed infants. Refined oils are commonly used as the main fat ingredient in formula manufacturing. For this study, different infant formula products (powders, concentrates and ready-to-feed formula products) were purchased and analysed in 2015 (35 samples) and 2019 (33 samples). Seven individual GEs were analysed by LC-MS/MS via direct approach by stable isotope dilution analysis, and total bound glycidol concentrations were calculated. Concentrations of bound glycidol in reconstituted formula reached maxima of 40.3 ng/g in the 2015 samples and 31.5 ng/g in the samples collected in 2019, with respective means of 8.7 ng/g and 6.7 ng/g. The analysed bound glycidol concentrations are comparable with concentration ranges from other studies, but are higher than observed in studies from the European market. Temporal trend data show a reduction of bound glycidol concentrations in 2019. GE concentrations were compared across different manufacturers.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.016
GPT teacher head0.253
Teacher spread0.237 · 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 designObservational
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

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueFood Additives & Contaminants Part ASame topicCarcinogens and Genotoxicity AssessmentFrench-language works237,207