Advances in our understanding of the structure and functionality of edible fats and fat mimetics
Bibliographic record
Abstract
The reasons for the increased world-wide incidence of obesity, type-2 diabetes, and cardiovascular disease include sedentary lifestyles and poor food choices. Regulatory agencies in several countries now require companies to add unattractive front of package labels to their products where salt, sugar and fat (or saturated fat) levels are prominently displayed. After the demise of partially hydrogenated fats, saturated fat has become the new target. Consumption of saturated fat over polyunsaturated oil has been clearly shown to increase cholesterol levels in humans. However, saturated fats provide the functionality required in many food products. To complicate matters, concerns over sustainability, veganism, genetically modified organisms, animal welfare, as well as religious beliefs, severely limit our sources of saturated fat. In this review we will discuss recent advances in our understanding of the nano and mesoscale structure of fats, responsible for their physical functionality and contrast it to that of fat mimetics. Fat mimetics include polymeric networks of ethylcellulose, emulsion-templated networks of proteins and polysaccharides, colloidal and self-assembled fibrillar networks of polar lipid crystals, as well as solid o/w emulsions of oil trapped within crystallized lamellar mesophases. Clean label and economic considerations will also be touched upon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".