Collagen peptide-loaded W<sub>1</sub>/O single emulsions and W<sub>1</sub>/O/W<sub>2</sub> double emulsions: influence of collagen peptide and salt concentration, dispersed phase fraction and type of hydrophilic emulsifier on droplet stability and encapsulation efficiency
Bibliographic record
Abstract
Bioactive peptides hold great promise as functional ingredients, but they are susceptible to hydrolysis (e.g., protease degradation) during digestion and under different physiological conditions in the human body. Thus, bioactive peptides must be encapsulated first to preserve their innate characteristics and to ensure delivery to an absorption site at the small intestine without biodegradation. The objective of this study was to formulate stable single emulsions of water in oil (W1/O) and double emulsions of water in oil in water (W1/O/W2) containing collagen peptide as core material. In this study, the influence of the following parameters was investigated: collagen peptide concentration (0-20%), salt (0% or 1% NaCl), hydrophilic emulsifiers for the outer water phase (Tween 80, lecithin, chitosan, pectin), and the use of different inner/outer water fractions (20-50% inner water, 50-80% outer water) on the droplet stability of the W1/O and W1/O/W2 emulsions during storage. In particular, the influence of the osmolytes was investigated to evaluate the change in osmotic balance and destabilization of the W1/O/W2 emulsion system as the encapsulated peptide was also osmotically active. Tween 80-stabilized W1/O/W2 emulsions showed the best droplet stability without phase separation (d90,3 = 36.6 μm) and the highest collagen peptide retention (encapsulation efficiency > 90%). During prolonged storage, collagen peptides were released from the W1/O/W2 emulsion system without significant droplet size changes, possibly due to diffusion of the collagen peptide from the inner to the outer water phase. Therefore, our findings can be utilized for preparing stable functional foods or cosmetic products from W1/O/W2 emulsions containing bioactive ingredients.
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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 | 0.000 | 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".