Impact of Canolol‐Enriched Extract from Heat‐Treated Canola Meal to Enhance Oil Quality Parameters in Deep‐Frying: a Comparison with Rosemary Extract and TBHQ‐Fortified Oil Systems
Bibliographic record
Abstract
Abstract Canolol‐enriched extracts obtained from the extraction of fluidized bed treated canola meal with supercritical carbon dioxide were added to high‐oleic canola oil in different concentrations (200, 500 and 750 mg/kg). After 30 h of deep‐fat frying, oils fortified with canolol‐enriched extracts showed a two to three times better frying performance in comparison to the commonly used antioxidants (TBHQ, 200 mg/kg; rosemary extract, 40 and 200 mg/kg) and a control without antioxidants with regards to the formation of di‐ and polymer triacylglycerols, total polar compounds, secondary degradation products (anisidine value) and the iodine value. The canolol‐enriched extracts were also able to slow down the degradation of α‐ and γ‐tocopherol during frying resulting in significant amounts of tocopherols after 30 h of frying in comparison to the other oils. The influence of the canolol‐enriched extracts indicated strongly concentration‐dependent performance. With increasing concentration of the extract, the thermal stability of the fortified oil was improved. The only disadvantage of the addition of the extracts was an increase in the initial acid value, but within the frying time, only oil fortified with 750 mg canolol‐enriched extract/kg reached the limit given in different countries.
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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) | 0.000 | 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".