Influence of cheese matrix on lipid digestion in a simulated gastro-intestinal environment
Bibliographic record
Abstract
Foods are complex nutrient assemblies which are subjected to various industrial processes that can influence their nutritional value. The food matrix acts as a nutrient-release regulator, and further understanding of its behavior during digestion is essential. The objective of this study was to compare the kinetics of matrix degradation and fatty acids release for different cheeses in a gastro-intestinal environment. The relationship between the physical characteristics of the cheeses (rheological properties, microstructure) and their digestion pattern was also studied. Rheological measurements, compositional and microstructure analyses were performed on mild Cheddar, aged Cheddar, light Cheddar and Mozzarella cheeses. Cheese samples were subjected to simulated digestion. Matrix degradation index, free oil, free fatty acids and fat droplet distribution were analyzed after 5, 30, 60, 120, 150, 180 and 240 min of digestion. Mozzarella cheese showed the highest rate of matrix degradation, free oil and fatty acids release. Aged Cheddar cheese showed rapid degradation during the gastric phase, but was more resistant to the duodenal environment. Light Cheddar showed the opposite behavior, being highly resistant to the gastric environment; however, it underwent extensive degradation at the end of the duodenal phase. The extent of matrix degradation for mild Cheddar was similar to that of Mozzarella in the gastric phase but was much lower than that of other cheeses in the duodenal phase. The cheeses under study exhibited very different digestion patterns, and these differences are discussed in relation to cheese matrix composition, microstructure and rheological properties. Results suggest that cheese degradation and kinetics of fatty acids release are mainly driven by cheese physical characteristics.
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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 | 0.000 |
| 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 | 0.001 | 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".