Disintegration kinetics of food gels during gastric digestion and its role on gastric emptying: an in vitro analysis
Bibliographic record
Abstract
The understanding of the disintegration and gastric emptying of foods in the stomach is important for designing functional foods. In this study, a dynamic stomach model (human gastric simulator, HGS) was employed to investigate the disintegration and subsequent emptying of two differently structured whey protein emulsion gels (soft and hard gels).The gels were mechanically ground into fragments to reproduce the particle size distribution of an in vivo gel bolus. The simulated gel bolus was prepared by mixing gel fragments and artificial saliva, and exposed to 5 hours of simulated gastric digestion in the presence and absence of pepsin. Results showed that regardless of pepsin, the soft gel always disintegrated faster than the hard gel. The presence of pepsin significantly accelerated the disintegration of both gels. In particular, it enhanced abrasion of the soft gel into fine particles (<0.425 mm) after 180 min of processing. The emptying of the gels was influenced by the combined effects of the original particle size of the gel boluses and their disintegration kinetics in the HGS. In the presence or absence of pepsin, the larger particles of the soft gel emptied slower than the hard one during the first 120 min of process. However, in the presence of pepsin, the soft gel emptied faster than the hard one after 120 min because of a higher level of disintegration. These findings highlight the role of food structure, bolus properties and biochemical effects on the disintegration and gastric emptying patterns of gels during gastric digestion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".