Monitoring the Epoxidation of Canola Oil by Non‐aqueous Reversed Phase Liquid Chromatography/Mass Spectrometry for Process Optimization and Control
Bibliographic record
Abstract
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.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".