Storage Stability and Physical Characteristics of Tea-Polyphenol-Bearing Nanoliposomes Prepared with Milk Fat Globule Membrane Phospholipids
Bibliographic record
Abstract
The objective of this work was to better understand the functional properties of milk phospholipids when used as ingredients to prepare liposomes. Liposomal dispersions (10%) were prepared using high-pressure homogenization, and their physical properties as well as their ability to encapsulate tea polyphenols were investigated. The extent of encapsulation, measured by HPLC, increased with tea polyphenol concentration up to about 4 mg·mL(-1). At polyphenol concentrations ≥ 6 mg·mL(-1), the liposome dispersions were no longer stable. The influence of pH (3-7), storage temperature (room temperature or refrigeration), and addition of sugars (0-15%) were studied for liposomes containing 4 mg·mL(-1) polyphenols. The liposomal dispersions were also stable in the presence of peptides. The storage stability of the systems prepared with milk phospholipids was compared to that of liposomes made with soy phospholipids. Soy liposomes were smaller in size than milk phospholipid liposomes, the encapsulation efficiency was higher, and the extent of release of tea polyphenols during storage was lower for milk phospholipid liposomes compared to soy liposomes. The results suggest that milk phospholipids could be employed to prepare tea-polyphenol-bearing liposomes and that the tea catechins may be incorporated in the milk phospholipid bilayer more efficiently than in the case of a soy phospholipid bilayer.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".