Micronutrient content of cold‐pressed, hot‐pressed, solvent extracted and RBD canola oil: Implications for nutrition and quality
Bibliographic record
Abstract
In this study the quality characteristics and content of healthy minor components of four crude canola oils as an effect of different oil extraction method (solvent extraction, hot pressing, and cold pressing) were studied. Cold‐pressed canola oils had lower concentrations of FFA, PV, p ‐AV and chlorophylls than solvent‐extracted, and hot‐pressed canola oils. Oils obtained via the different extraction methods had different fatty acid profiles as well as dissimilar amounts of tocopherols, phytosterols, and polyphenols. The amount of total tocopherols in solvent‐extracted canola oil was 493 mg/kg compared to 388 mg/kg for hot‐pressed canola oil. The tocopherol content for two other cold‐pressed and one other RBD canola oil was 366, 354, and 327 mg/kg, respectively. Solvent‐extracted canola oil exhibited the highest free phytosterol content (178 mg/100 g), while RBD canola oil only had 129 mg/100 g of free phytosterols. While cold‐pressed canola oil had the lowest amount of polyphenols, traditional refining resulted in almost complete removal of polyphenols from canola oil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".