Predictive Response Surface Model for Heat‐Induced Rheological Changes and Aggregation of Whey Protein Concentrate
Bibliographic record
Abstract
Whey proteins are now far more than a by-product of cheese processing. In the last 2 decades, food manufacturers have developed them as ingredients, with the dairy industry remaining as a major user. For many applications, whey proteins are modified (denatured) to alter their structure and functional properties. The objective of this research was to study the influence of 85 to 100 °C, with protein concentration of 8% to 12%, and treatment times of 5 to 30 min, while measuring rheological properties (storage modulus, loss modulus, and complex viscosity) and aggregation (intermolecular beta-sheet formation) in dispersions of whey protein concentrate (WPC). A Box-Behnken Response Surface Methodology modeled the heat denaturation of liquid sweet WPC at 3 variables and 3 levels. The model revealed a very significant fit for viscoelastic properties, and a lesser fit for protein aggregation, at temperatures not previously studied. An exponential increase of rheological parameters was governed by protein concentration and temperature, while a modest linear relationship of aggregation was governed by temperature. Models such as these can serve as valuable guides to the ingredient and dairy industries to develop target products, as whey is a major ingredient in many functional foods.
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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 | 0.004 | 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 | 0.000 | 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 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".