Construction of protein-, polysaccharide- and polyphenol-based conjugates as delivery systems
Bibliographic record
Abstract
Natural polymers, such as polysaccharides and proteins, have been used to prepare several delivery systems owing to their abundance, bioactivity, and biodegradability. They are usually modified or combined with small molecules to form the delivery systems needed to meet different needs in food systems. This paper reviews the interactions of proteins, polysaccharides, and polyphenols in the bulk phase and discusses the design strategies, coupling techniques, and their applications as conjugates in emulsion delivery systems, including traditional, Pickering, multilayer, and high internal-phase emulsions. Furthermore, it explores the prospects of the application of conjugates in food preservation, food development, and nanocarrier development. Currently, there are seven methods for composite delivery systems including the Maillard reaction, carbodiimide cross-linking, alkali treatment, enzymatic cross-linking, free radical induction, genipin cross-linking, and Schiff base chemical cross-linking to prepare binary and ternary conjugates of proteins, polysaccharides, and polyphenols. To design an effective target complex and its delivery system, it is helpful to understand the physicochemical properties of these biomolecules and their interactions in the bulk phase. This review summarizes the knowledge on the interaction of biological complexes in the bulk phase, preparation methods, and the preparation of stable emulsion delivery system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".