Regulatory Changes Affecting the Production and Use of Fats and Oils: Focus on Partially Hydrogenated Oils
Bibliographic record
Abstract
Abstract Partially hydrogenated oils (PHO), the products of incomplete catalytic hydrogenation of food oils, have been widely employed by the food industry for more than a century. Their exceptional stability and technologic characteristics made them the preferred choice for the production of several food products including margarines, bakery goods, and frying oils. Some of these highly prized characteristics were provided by the high content in trans fatty acids (TFA), defined as fatty acids with one or more isolated double bond in trans configuration. The discovery of negative health effects associated with dietary intake of TFA triggered world‐wide a wave of regulatory actions aimed to curb their consumption. PHO became the main target of most campaigns aimed to reduce consumption of TFA, and their fortune in the food industry progressively faded. At the 2017, AOCS Annual Meeting in Orlando, a group of experts from regulatory agencies and industry from North America and Europe met to discuss the current status of government regulations and industry adaptations regarding the productions and use of PHO. The discussion was enriched by including the impact on fats and oils production of the 2016 amendment of the Toxic Substances Control Act of 1976 (TSCA). The present publication may not include all regulatory changes that took place after this symposium, in 2017.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".