Behaviour of whey protein emulsion gel during oral and gastric digestion: effect of droplet size
Bibliographic record
Abstract
A set of whey protein stabilized-emulsion gels with different droplet size distributions (D4,3 = ∼1, 6 and 12 μm) was produced, and the mechanical properties of the gels in the linear viscoelastic region and at large deformation were measured, along with the physicochemical and structural changes of the gels during oral mastication and gastric digestion. The gels containing 1 μm oil droplets had an aggregated particle structure with proteins coating at oil droplets whereas the gels containing 12 μm oil droplets had a particle-filled structure with spatially continuous matrix. During oral processing, the release of oil droplets from the gels increased as the droplet size increased, with coalescence being seen in gels containing oil droplets of 6 and 12 μm diameter. Under gastric digestion, high degrees of coalescence and phase separation of oil droplets occurred in the gels containing 6 and 12 μm oil droplets because of oil droplet release from the gel matrix; this led to slow gastric emptying. The gels were finally broken down into peptide aggregates and oil droplets (or free oil). The gels, containing 1 μm oil droplets disintegrated into various particles of several to several tens of microns with a low degree of oil droplet release and coalescence. Protein breakdown was slower in these gels, suggesting that the protein structures of the gel matrices were affected by the sizes of the incorporated oil droplets.
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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 | 0.001 |
| 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 | 0.001 |
| 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".