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Record W584402538 · doi:10.1021/acs.jafc.5b01719

New <sup>1</sup>H NMR-Based Technique To Determine Epoxide Concentrations in Oxidized Oil

2015· article· en· W584402538 on OpenAlexafffund
Wei Xia, Suzanne M. Budge, Michael D. Lumsden

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2015
Typearticle
Languageen
FieldChemistry
TopicEdible Oils Quality and Analysis
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEpoxideChemistryHydrogen peroxideProton NMRAcetic acidBromideSoybean oilTitrationOrganic chemistryChromatographyNuclear chemistryCatalysisBiochemistry

Abstract

fetched live from OpenAlex

A new method to determine epoxide concentrations in oxidized oils was developed and validated using (1)H NMR. Epoxides derived from lipid oxidation gave signals between 2.90 and 3.24 ppm, well separated from the signals of other lipid oxidation products. To calibrate, soybean oils with a range of epoxide concentrations were synthesized and analyzed using (1)H NMR by taking the sn-1,3 glycerol protons (4.18, 4.33 ppm) as internal references. The (1)H NMR signals were compared to the epoxide content determined by titration with hydrogen bromide (HBr)-acetic acid solution. As expected, the signal response increased with concentration linearly (R(2) = 99.96%), and validation of the method gave results comparable to those of the HBr method. A study of the oxidative stability of soybean oil was performed by applying this method to monitor epoxides during thermal lipid oxidation. The epoxide content increased over time and showed a different trend compared to peroxide value (PV). A phenomenological model was suggested to model epoxides derived from lipid oxidation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.543

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 head0.023
GPT teacher head0.242
Teacher spread0.219 · 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

Citations43
Published2015
Admission routes2
Has abstractyes

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