Column Fractionation of Canola Oil Deodorizer Distillate Using Supercritical Carbon Dioxide
Bibliographic record
Abstract
Abstract Semi‐continuous column fractionation of canola oil deodorizer distillate using supercritical CO2 (SCCO2) was carried out to determine the feasibility of value‐added processing of this feed material for the recovery of bioactive components such as sterols and tocopherols and to determine the effect of operating conditions [pressure (20, 25 MPa using a temperature gradient of 70–100 °C), temperature (70, 100 °C) and a linear temperature gradient (70–100 °C at 25 MPa)] on extract yield and separation efficiency. Total extract yield increased significantly (p ≤ 0.05) with pressure, whereas at isobaric conditions (25 MPa) the highest yield was obtained at the lowest temperature tested (70 °C). Fractionation efficiency was reflected in the composition of fractions and was affected by operating conditions. Residue composition was determined by extract yield in addition to selectivity. Use of the thermal gradient (70–100 °C) decreased the content of volatiles, free fatty acids and tocopherols while increasing sterol content significantly (p ≤ 0.05) to a level of 40% (GC area %) in the residue obtained at 25 MPa. The findings indicate the potential of canola oil deodorizer distillate as a source of sterols and warrant further research on the countercurrent column fractionation to improve the separation efficiency.
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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".