Effect of homogenization and heat treatment on the behavior of protein and fat globules during gastric digestion of milk
Bibliographic record
Abstract
The effects of homogenization and heat treatment on the formation and the breakdown of clots during gastric digestion of whole milk were investigated using a human gastric simulator. Homogenization and heat treatment led to formation of coagula with fragmented and crumbled structures compared with the coagulum formed from raw whole milk, but a larger fraction of the protein and more fat globules were incorporated into the coagula induced by action of the milk-clotting enzyme pepsin. The fat globules in the whole milk appeared to be embedded in the clots as they formed. After formation of the clot, the greater numbers of pores in the structures of the clots formed with homogenized milk and heated whole milk led to greater rates of protein hydrolysis by pepsin, which resulted in faster release of fat globules from the clots into the digesta. Coalescence of fat globules occurred both in the digesta and within the protein clots no matter whether they were in homogenized or heated milk samples. The formation of clots with different structures and hence the changes in the rates of protein hydrolysis and the release of milk fat into the digesta in the stomach provide important information for understanding the gastric emptying of milk and the potential to use this knowledge to manipulate the bioavailability of fat and other fat-soluble nutrients in dairy products.
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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 | 0.001 | 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 | 0.001 |
| 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".