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Record W2200820960 · doi:10.1007/s11746-015-2780-7

Quantification of Nonanal and Oleic Acid Formed During the Ozonolysis of Vegetable Oil Free Fatty Acids or Fatty Acid Methyl Esters

2015· article· en· W2200820960 on OpenAlexafffund
Mohammad Hossein Tavassoli‐Kafrani, Patrick M. Foley, Ereddad Kharraz, Jonathan M. Curtis

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsUniversity of Alberta
FundersAlberta InnovatesUniversity of Alberta
KeywordsNonanalOleic acidOzonolysisChemistryOrganic chemistryFatty acidChromatographyBiochemistry

Abstract

fetched live from OpenAlex

Abstract The ozonolysis of unsaturated lipids is a process that has been used to generate aldehydes, acids, alcohols, and other biobased chemical intermediates. Reported here is a method that can be used to measure the formation of nonanal and oleic acid during the ozonolysis of unsaturated vegetable oil fatty acids or their methyl esters to indicate the extent of the ozonolysis reaction. Derivatization was performed using boron trifluoride in methanol solution to transform nonanal and oleic acid into nonanal dimethyl acetal and oleic acid methyl ester, respectively. Undecanal and 10‐heptadecenoic acid were used as internal standards and separation was performed using gas chromatography coupled with a flame ionization detector. The method was validated by performing a standard addition procedure in which nonanal or oleic acid standards were spiked into samples collected during the ozonolysis of oleic acid or canola oil fatty acid methyl ester (FAME). Linear regression results indicated that the measured nonanal and oleic acid are in good agreement with the actual amounts of nonanal and oleic acid added to the sample with at least 98 % recovery. The application of the method was demonstrated by the successful measurement of nonanal and oleic acid formed throughout the ozonolysis process for high oleic canola oil FAME.

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)0.001
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 score0.010
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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

Citations15
Published2015
Admission routes2
Has abstractyes

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