Effect of gel structure on the gastric digestion of whey protein emulsion gels
Bibliographic record
Abstract
This study aimed to characterize and determine the disintegration of emulsion gels in a human gastric simulator (HGS) and the physicochemical characteristics of gastric digesta. Using thermal treatment at 90 °C, whey protein emulsion gels with different structures and gel strengths were formed by varying the ionic strength. Simulated boluses of soft (containing 10 mM NaCl) and hard (200 mM NaCl) gels, which had similar particle sizes to those of human subjects, were created for gastric digestion. Soft gels disintegrated faster than hard gels in the HGS. The boluses of both gels gradually disintegrated into particles of size ∼10 μm. With further digestion, the protein matrix of the soft gel particles dissolved, the proteins were disrupted mainly by proteolysis and large quantities of oil droplets were released. In contrast, for the hard gel particles, although all proteins were hydrolysed after 240 min the breakdown of the particles was slow and no oil droplets were released after 300 min. The differences in the breakdown of soft and hard gels in the HGS were attributed to the structures of the emulsion gel, which may result in different sets of peptides in the digestion. In addition, coalescence of the oil droplets was observed only for the soft gel.
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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 | 0.000 |
| 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".