Wax‐based delivery systems: Preparation, characterization, and food applications
Bibliographic record
Abstract
The development of lipid-based delivery systems has attracted much attention over the last years and a wide variety of strategies and formulations are currently available to encapsulate, protect, and target delivery of bioactive and functional lipophilic constituents within the food and pharmaceutical industries. Waxes are crystalline lipid material, consisting of a complex mixture of long-chain fatty acids and fatty alcohols, hydrocarbons, aldehydes, and ketones and show great promises as constituents of carrier systems. Most of waxes are classified under food-grade category and show high availability at a low cost. This review article has provided a comprehensive summary of research on major carriers containing wax as one of the main constituents, including solid lipid nanoparticles, nanostructured lipid carriers, oleogels, and Pickering emulsions, with a focus on their food applications. The physical and chemical nature of natural waxes are described in the first while the second part deals with the structure, formulation, main methods of preparation, characterization, and finally utilization of each type of wax-based delivery system for specific food applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".