Minor Constituents in Canola Oil Processed by Traditional and Minimal Refining Methods
Bibliographic record
Abstract
Abstract The minimal refining method described in the present study made it possible to neutralize crude canola oil with Ca(OH)2, MgO, and Na2SiO3 as alternatives to NaOH. After citric acid degumming, about 98 % of the phosphorous content was removed from crude oil. The free fatty acid content after minimal neutralization with Ca(OH)2 decreased from 0.50 to 0.03 %. Other quality parameters, such as peroxide value, anisidine value, and chlorophyll content, after traditional and minimal neutralization were within industrial acceptable levels. The use of Trisyl silica and Magnesol R60 made it feasible to remove the hot‐water washing step and decreased the amount of residual soap to <10 mg/kg oil. There were no significant changes in chemical characteristics of canola oil after using wet and dry bleaching methods. During traditional neutralization, the total tocopherol loss was 19.6 %, while minimal refining with Ca(OH)2, MgO, and Na2SiO3 resulted in 7.0, 2.6, and 0.9 % reductions in total tocopherols. Traditional refining removed 23.6 % of total free sterols, while after minimal refining free sterols content did not change. Both traditional and minimal refining resulted in almost complete removal of polyphenols from canola oil. Total phytosterols and tocopherols in two cold‐pressed canola oils were 774 and 836 mg/100 g, and 366 and 354 mg/kg, respectively. The minimal refining method described in the present study was a new practical approach to remove undesirable components from crude canola oil meeting commercial refining standards while preserving more healthy minor components.
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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 | 0.001 | 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".