Air frying as a heat pre-treatment method for improving the extraction and yield of canolol from canola seeds
Bibliographic record
Abstract
Abstract Roasting of canola seeds prior to oil extraction is essential for producing unique flavors and heat-induced formation of novel phenolic compounds. This study investigated the efficacy of air frying as a seed roasting pre-treatment technique to improve the recovery of canolol and other oil-soluble sinapic acidic derivatives (SADs) from canola seeds. Air frying of canola seeds was carried out at temperature-time regimens of 160, 170, 180 or 190 ◦C for 5, 10, 15, or 20 min, respectively. Oil was extracted by the Soxtec method and the soluble SADs were extracted from the oil by addition of an equal volume of hexane/70% methanol mixture followed by quantification using high performance liquid chromatography-diode array detection (HPLC-DAD). The total phenolic content (TPC) and antioxidative activity of the oils were also evaluated. The results indicated a time-temperature association for the recovery of canolol. The optimum air frying condition at 190 oC for 15 min produced the maximum yield of canolol (1439 ± 45.6 μg/100 g roasted seed) in addition to other unidentified SADs. The oil extracts obtained from canola seed roasted at 180, 15 min, 190 oC, 15 and 20 min showed the highest TPC (0.387 ± 0.015, 0.413 ± 0.002 and 0.419 ± 0.002 mg GAE/g oil), respectively, and the strongest antioxidant activities (DPPH radical scavenging and iron reducing), but exhibited weak metal ion chelating activity. There was a strong positive correlation of canolol content to the antioxidant activities (DPPH, FRAP) and TPC value for the canola oil extracts (r=0.85, r=0.93 and 0.88), respectively.
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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".