Bibliographic record
Abstract
Abstract There is a growing body of evidence suggesting that high fat intake may specifically contribute to heart disease, overweight, and obesity. Dietary recommendations are to reduce the current fat intake to 20–35% of total calories. In response to consumer demands for low calorie or calorie‐free fats, a wide array of fat replacers have been developed. Fat replacers are substances that contribute a similar mouthfeel, texture, or flavor to a food product as normal fat. There are three major fat‐replacement ingredient categories: carbohydrate, protein, and fat based. Carbohydrate‐ and protein‐based fat replacers provide 0–4 kcal/g. The major carbohydrate‐based fat replacers include starches, maltodextrins, polydextrose, pectin, gums, and other dietary fibers. Examples of protein‐based fat replacers are microparticulated protein, gelatin, and modified–denatured proteins. Some fat‐based ingredients, eg, caprenin, salatrim, bohenin, and medium‐chain triacylgycerols, are actually fats tailored to contribute fewer calories and less available fat to foods. Lipid analogues, eg, olestra, sorbestrin, and polyglycerol esters, are structurally modified to provide fewer or no calories. With successful marketing and improvements in process technology and quality, fat replacers will have a considerable impact upon most sectors in the food industry. The different types of fat replacers are reviewed with respect to their synthesis, chemical composition, caloric value, metabolism, and applications.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.102 | 0.060 |
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".