Trans fatty acids content in worldwide edible fats and oils: current trends and challenges
Bibliographic record
Abstract
Trans fatty acids (TFA) have been related with multiple cardiovascular risk factors and higher risk of coronary heart disease. Partial hydrogenation, which converts liquid vegetable oils into solid or semi-solid fats with appropriate melting properties suitable for the production of shortenings and margarines, is one of the sources of TFA. In this study a worldwide comparison regarding the TFA content in edible oils and fats was performed. Oils and fats, from vegetable or animal origin, are essential ingredients for a variety of food products. The major dietary sources of TFA are foods containing partially hydrogenated vegetable oils, namely, shortenings and/or margarines, and animal fats as butter, while edible vegetable oils, in general have low contents. The content of TFA of vegetable oils can increase when subjected to drastic heating, for example deep-fat frying or oven baking. However, TFA formation strongly depends on several factors, namely, frying conditions (type of fryer, duration and temperature), frying material (oil/fat and the food itself), among others. In conclusion, a great variability between countries was observed for example for margarines, where Canada was one of the countries with the highest percentage of TFA (42.9% of total fatty acids) for margarines produced with partially hydrogenated vegetable oils. On the other hand, in Germany, Portugal, Austria and Canada as well, the identified margarines with lower content of TFA are mainly produced with nonhydrogenated fats. With respect to edible vegetable oils, in general, these have lower contents of TFA than margarines, shortenings and butters, as expected.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".