A Rapid Method for Determining the Oxidative Stability of Oils Suitable for Breeder Size Samples
Bibliographic record
Abstract
A method utilizing thin-layer chromatography with a flame ionization detector (TLC-FID) was developed for assessing the stability of breeder's oil seed samples based on the formation of polar compounds. The results showed a linear relationship between peroxide value (PV) and the content of polar material in the oxidized oil. Oil samples oxidized very readily on chromarods, even at low temperature, which is a particular advantage for antioxidant screening. At 45 °C, the oil oxidation rate was relatively low, but the relationship between the content of polar material and reaction time was linear. At 65 °C, if the content of polar material was below 50 %, the above relationship was still linear. At different temperatures, the action of tocopherol appeared to vary slightly. For example, at 65 °C, the oxidative stability of the oil sample was determined by the content of tocopherol, especially γ-tocopherol. At 45 and 55 °C, the oxidative stability was determined by both the content of tocopherol and polyunsaturated fatty acids. Of the tocopherol isomers, γ-tocopherol exhibited the highest antioxidant potency, consistent with the published literature. These results suggest that chromarods provide good media for monitoring oil oxidation for antioxidant screening. A particular advantage is the use of very small oil samples, usually 1-2 μL, and the ability to analyze multiple samples at the same time.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".