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Record W2011592501 · doi:10.1007/s11746-012-2103-1

Monitoring the Epoxidation of Canola Oil by Non‐aqueous Reversed Phase Liquid Chromatography/Mass Spectrometry for Process Optimization and Control

2012· article· en· W2011592501 on OpenAlexafffund
Sabiqah Tuan Anuar, Yuan Zhao, Samuel M. Mugo, Jonathan M. Curtis

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2012
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsMacEwan UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Crop Industry Development Fund
KeywordsChemistryDouble bondMass spectrometryDegree of unsaturationChromatographyElectrosprayAqueous solutionCanolaAqueous two-phase systemOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Non‐aqueous reversed phase liquid chromatography/electrospray mass spectrometry (NARP‐LC/ESI–MS) was used to monitor the epoxidation of canola oil by performic acid. The reaction was sampled at regular intervals over 28 h and analyzed by NARP‐LC/ESI–MS in order to observe the formation of partially epoxidized reaction intermediates and the fully epoxidized products. The experiment focused on the transformation of triacylglycerols (TAG) with 54 carbons in the fatty acyl chains and between 2 and 7 double bonds which account for >93 % of the oil. NARP‐LC/ESI–MS allowed determination of the time required for full epoxidation of the oil. It was shown that complete epoxidation of TAG with low numbers of double bonds occurs more rapidly than for those with many double bonds. Furthermore, it was observed that epoxidation of multiply unsaturated TAG occurs via a sequential process in which partially epoxidized intermediates are consumed to form other more highly epoxidized compounds as the reaction proceeds. Data obtained by flow‐injection ESI–MS was found to be comparable to that obtained from NARP‐LC/ESI–MS for monitoring intermediates and products and could be adapted for in‐process reaction monitoring.

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 score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.006
GPT teacher head0.257
Teacher spread0.251 · 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

Citations25
Published2012
Admission routes2
Has abstractyes

Explore more

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