Yield and Composition of Grape Seed Oils Extracted by Supercritical Carbon Dioxide and Petroleum Ether: Varietal Effects
Bibliographic record
Abstract
Grape seed has a well-known potential for production of oil as a byproduct of winemaking and is currently produced as a specialty oil byproduct of wine manufacture. Seed oils from eight varieties of grapes crushed for wine production in British Columbia were extracted by supercritical carbon dioxide (SCE) and petroleum ether (PE). Oil yields by SCE ranged from 5.85 +/- 0.33 to 13.6 +/- 0.46% (w/w), whereas PE yields ranged from 6.64 +/- 0.16 to 11.17 +/- 0.05% (+/- is standard deviation). The oils contained alpha-, beta-, and gamma-tocopherols and alpha- and gamma-tocotrienols, with gamma-tocotrienol being most important quantitatively. In both SCE- and PE-extracted oils, phytosterols were a prominent feature of the unsaponifiable fraction, with beta-sitosterol quantitatively most important with both extractants. Total phytosterol extraction was higher with SCE than with PE in seven of eight variety extractions. Fatty acid composition of oils from all varieties tested, and from both extraction methods, indicated linoleic acid as the major component ranging from 67.56 to 73.23% of the fatty acids present, in agreement with literature reports.
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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 | 0.001 |
| 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".