New <sup>1</sup>H NMR-Based Technique To Determine Epoxide Concentrations in Oxidized Oil
Bibliographic record
Abstract
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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".