Effect of commercial grape extracts on the cheese-making properties of milk
Bibliographic record
Abstract
Grape extracts can be added to milk to produce cheese with a high concentration of polyphenols. Four commercial extracts from whole grape, grape seed, and grape skin (2 extracts) were characterized and added to milk at concentrations of 0, 0.1, 0.2, and 0.3% (wt/vol). The effect of grape extracts on the kinetics of milk clotting, milk gel texture, and syneresis were determined, and model cheeses were produced. Whole grape and grape seed extracts contained a similar concentration of polyphenolic compounds and about twice the amount found in grape skin extracts. Radical scavenging activity was directly proportional to the phenolic compounds content. When added to milk, grape extracts increased rennet-induced clotting time and decreased the clotting rate. Although differences were observed between the extracts, the concentration added to milk was the main factor influencing clotting properties. With increasing concentrations of grape extracts, milk gels showed increased brittleness and reduced firmness. In addition, syneresis of milk gels decreased with increasing concentrations of grape extracts, which resulted in cheeses with a higher moisture content. The presence of grape extracts in milk slightly increased protein recovery in cheese but had no effect on fat recovery. With whole grape or grape seed extracts added to milk at 0.1% (wt/vol), the recovery coefficient for polyphenols was about 0.63, and decreased with increasing extract concentration in milk. Better polyphenol recovery was observed for grape seed extracts (0.87), with no concentration effect. Commercial extracts from whole grape, grape seed, or grape skin can be added to milk in the 0.1 to 0.3% (wt/vol) concentration range to produce cheese with potential health benefits, without a negative effect on cheese yield.
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How this classification was reachedexpand
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 | 0.005 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".