Frying Performance of Canola Oil Triacylglycerides as Affected by Vegetable Oils Minor Components
Bibliographic record
Abstract
Abstract The endogenous minor components from canola, rice bran, sesame and palm oils including selected phospholipids, and various combinations of tocopherol isomers were tested during frying using canola oil triacylglycerols as the frying medium. Thermo‐oxidative degradation was assessed by measurement of the total polar components, the rate of volatile carbonyl compounds and 4‐hydroxynonenal formation. All the tested minor components protected to a different extent canola triacylglycerides from thermo‐oxidative degradation during frying. No significant differences were observed in the protection of the triacylglycerides among all the tested tocopherol isomers and their mixtures. Irrespective of the composition of tocopherol homologous, an increase in the added amounts above 1,000 μg/g did not improve protection. Minor components isolated from rice bran and sesame oils offered better protection during canola triacylglycerides frying than endogenous minor components isolated from canola oil. When 0.2% phosphatidylcholine or phosphatidylethanolamine was added to the canola triacylglycerides, the amount of formed polar components decreased twice as compared to the tocopherol isomers. Accordingly, by optimizing the composition and the concentration of the endogenous minor components, the frying performance of oil can be significantly enhanced.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".