Tocopherols and Tocotrienols in Common and Emerging Dietary Sources: Occurrence, Applications, and Health Benefits
Bibliographic record
Abstract
Edible oils are the major natural dietary sources of tocopherols and tocotrienols, collectively known as tocols. Plant foods with low lipid content usually have negligible quantities of tocols. However, seeds and other plant food processing by-products may serve as alternative sources of edible oils with considerable contents of tocopherols and tocotrienols. Tocopherols are among the most important lipid-soluble antioxidants in food as well as in human and animal tissues. Tocopherols are found in lipid-rich regions of cells (e.g., mitochondrial membranes), fat depots, and lipoproteins such as low-density lipoprotein cholesterol. Their health benefits may also be explained by regulation of gene expression, signal transduction, and modulation of cell functions. Potential health benefits of tocols include prevention of certain types of cancer, heart disease, and other chronic ailments. Although deficiencies of tocopherol are uncommon, a continuous intake from common and novel dietary sources of tocopherols and tocotrienols is advantageous. Thus, this contribution will focus on the relevant literature on common and emerging edible oils as a source of tocols. Potential application and health effects as well as the impact of new cultivars as sources of edible oils and their processing discards are presented. Future trends and drawbacks are also briefly covered.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".