Products Formed During Thermo‐oxidative Degradation of Phytosterols
Bibliographic record
Abstract
Abstract Oxidative degradation of cholesterol has been extensively researched, however, not all products formed have been established. When a phytosterols standard was heated at 60, 120 and 180 °C for different period of time the following groups of components were detected: oxidized phytosterols, fragmented phytosterol molecules, volatile compounds and oligomers. Taking into account all the components formed, we were able to balance the amounts of disappearing sterols with components formed. We established that the amount and type of products formed during thermo‐oxidative degradation is affected by temperature and time. The amount of intact phytosterols decreased when temperature and time increase. The amount of oxidized phytosterols was at the highest level when a temperature of 120 °C was applied, whereas the lowest amounts were observed when a temperature of 60 °C was used. At a temperature of 180 °C the amount of oxidized sterols was lower than at 120 °C and it decreased when the heating time was increased. This indicates that oxidized sterols were the main precursors involved in the formation of other components during thermo‐oxidative degradation. The amount and type of volatile compounds formed increased when time and temperature increased. We observed diversified groups of volatile compounds formed and most of them are defined as off‐flavor compounds for rancid oils.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".