Interaction of green tea polyphenols with dairy matrices in a simulated gastrointestinal environment
Bibliographic record
Abstract
The consumption of polyphenols in green tea has been associated with beneficial health effects. Although polyphenols are unstable in the intestinal environment, they may be protected by interactions with dairy proteins during digestion. The objectives of this study were to evaluate the effect of a green tea extract on the digestibility of different dairy matrices and to monitor the antioxidant activity of these matrices with or without the green tea extract during digestion in a simulated gastrointestinal environment. Milk, yogurt and cheese with similar fat-to-protein ratios were subjected to simulated digestion. Matrix degradation, protein and fat hydrolysis, polyphenol concentration and radical scavenging activity were analyzed during gastric and intestinal digestion phases. Cheese was the matrix most resistant to protein and fat digestion. The addition of the green tea extract significantly decreased proteolysis in the gastric phase but had no effect in the intestinal phase. The kinetics of fatty acid release was reduced by the presence of the green tea extract. Transition from the gastric phase to the intestinal phase induced a 50% decrease in the antioxidant activity of the control (tea extract dispersed in water) due to the degradation of polyphenols. The presence of dairy matrices significantly improved polyphenol stability in the intestinal phase and increased the antioxidant activity by 29% (cheese) to 42% (milk) compared to the control. These results suggest that simultaneous consumption of green tea and dairy products helps to maintain the integrity and antioxidant activity of polyphenols during digestion.
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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 | 0.000 | 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 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".