Whey protein-derived biomaterials and their use as bioencapsulation and delivery systems
Bibliographic record
Abstract
The emergence of bioactive food compounds (nutraceutical compounds) with health benefits provides an excellent opportunity for improving public health. The incorporation of bioactive compounds into food systems is therefore of great interest to researchers in their efforts to develop innovative functional foods that may have physiological benefits or reduce the risk of disease beyond basic nutritional functions. However, the effectiveness of these products in preventing diseases relies on preserving the bioavailability of their active ingredients. This represents undoubtedly a great challenge since these molecules are generally sensitive to environmental conditions encountered in food processes (i.e., temperature oxygen, and light) or in the gastrointestinal tract (i.e., pH, enzymes presence of other nutrients), which limit their activity and potential health benefits. However, bio- and microencapsulation can be used to overcome these limitations. Whey proteins, also known as the serum proteins of milk, are widely used in food products, because of their high nutritional value and their ability to form gels, emulsions, or foams. The aim of this article is to provide information on the different types of materials obtained from whey proteins and to examine their use as bioencapsulation and delivery systems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".