Optimization of food-grade colloidal delivery systems for thermal processing applications: a review
Bibliographic record
Abstract
Colloidal delivery systems are widely used in the food industry to enhance the dispersibility, stability, efficacy, or bioavailability. However, when exposed to the high temperature, delivery systems are often prone to degradation, which limits its application in thermal processing. In this paper, the effects of thermal processing on the performance of traditional protein-based or starch-based delivery systems are firstly described, including the molecular structure changes of proteins, starches or lipids, and the degradation of embedded substances. These effects are unfavorable to the application of the delivery system in thermal processing. Then, strategies of improving the heat resistance of food grade colloid delivery system and their use in frying, baking and cooking food are mainly introduced. The heat resistance of the delivery system can be improved by a variety of strategies, including the development of new heat-resistant materials, the addition of heat-resistant coatings to the surface of delivery systems, the cross-linking of proteins or starches using cross-linking agents, the design of particle structures, the use of physical means such as ultrasound, or the optimization of the ingredient formula. These strategies will help to expand the application of heat-resistant delivery systems so that they can be used in real thermal processing.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".