Use of starch‐based fat replacers in foods as a strategy to reduce dietary intake of fat and risk of metabolic diseases
Bibliographic record
Abstract
Cardiovascular disease (CVD) has emerged as one of the leading causes of death worldwide. Elevated blood cholesterol and low-density lipoprotein levels are crucial risk factors that contribute to the development of CVD and other metabolic diseases. Dietary fat is believed to be the key factor in modulating circulating cholesterol levels. Thus, reducing dietary intake of fat appears to be an effective strategy to reduce the risk of heart disease. Also, excessive intake of fat and high-calorie foods is also related to the development of obesity, which contributes to the development of CVD. Therefore, the consumption of low-fat low-calorie foods is part of a healthier dietary pattern. However, simply removing fat from foods may lead to compromised overall quality and reduced acceptance of the food products. Thus, fat replacers have emerged as ideal alternatives to dietary fat, which can not only reduce the overall fat and calorie content of the foods but also mimic the physiochemical properties of dietary fat. Starch-based fat replacers are one kind of fat mimetic that can be produced either chemically as modified starch or enzymatically as maltodextrins. Both modified starch and maltodextrins have been demonstrated to have a promising ability to improve the overall quality of reduced-fat foods. Modified starch granules act directly as fat globules in modulating the structure and sensory characteristics of the foods, whereas maltodextrins can form thermoreversible gels. Both modified starch granules and maltodextrins can create a fat-like mouthfeel and therefore are potential fat replacers. This review article aims to discuss the following topics: (a) the effect of carbohydrates and fat on human cardiovascular health and other disease risks, (b) the functionality of starch-based fat replacers in foods, (c) the applications of starch-based fat replacers in various foods, and (d) the current and future market value of starch-based fat replacers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".