Quantification of Nonanal and Oleic Acid Formed During the Ozonolysis of Vegetable Oil Free Fatty Acids or Fatty Acid Methyl Esters
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".