Disintegration kinetics of food gels during gastric digestion and its role on gastric emptying: an in vitro analysis
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it