A robust stripping method for the removal of minor components from edible oils
Bibliographic record
Abstract
Abstract Column chromatographic techniques have commonly been used for effective stripping of edible oils from their minor components. However, this method is time consuming, which may lead to oil oxidation. Thus, in the present study, the oils of camelina seed, chia seed, sophia seed, corn, olive, and a docosahexaenoic acid single cell oil (DHASCO) were subjected to a simplified stripping method by using the stationary phase material and examining their minor components such as tocopherols, carotenoids, and chlorophylls as well as their oxidative stability. The results demonstrated that stripped oils prepared by using the simplified stripping method for 2 h were devoid of any tocopherol, chlorophylls and carotenoids and this was as effective as column chromatographic method. Thus, the simplified stripping method provides a facile means of producing stripped oil with better oxidative stability compared to the column chromatographic method. Graphical abstract
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".