Effect of denatured whey protein concentrate and its fractions on cheese composition and rheological properties
Bibliographic record
Abstract
The objectives of this study were (1) to assess the effect of a denatured whey protein concentrate (DWPC) and its fractions on cheese yield, composition, and rheological properties, and (2) to separate the direct effect of the DWPC or its fractions on cheese rheological properties from the effect of a concomitant increase in cheese moisture. Semihard cheeses were produced at a laboratory scale, and mechanical properties were characterized by dynamic rheometry. Centrifugation was used to induce a moisture gradient in cheese to separate the direct contribution of the DWPC from the contribution of moisture to cheese mechanical properties. Cheese yield increased and complex modulus (G*) decreased when the DWPC was substituted for milk proteins in milk. For cheeses with the same moisture content, the substitution of denatured whey proteins for milk proteins had no direct effect on rheological parameters. The DWPC was fractionated to evaluate the contribution of its different components (sedimentable aggregates, soluble component, and diffusible component) to cheese yield, composition, and rheological properties. The sedimentable aggregates were primarily responsible for the increase in cheese yield when DWPC was added. Overall, moisture content explained to a large extent the variation in cheese rheological properties depending on the DWPC fraction. However, when the effect of moisture was removed, the addition of the DWPC sedimentable fraction to milk increased cheese complex modulus. Whey protein aggregates were hypothesized to act as active fillers that physically interact with the casein matrix and confer rigidity after pressing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".