Rheological Properties of Rennet Gels Containing Milk Protein Concentrates
Bibliographic record
Abstract
Different milk protein concentrates (MPC), with protein concentrations of 56, 70, and 90%, were dispersed in water under different treatments (hydration, shear, heat, and overnight storage at 4 degrees C), as well as in a combination of all the treatments in a factorial design. The particle size distribution of the dispersions was then measured to determine the optimal conditions for the dispersion. Heating at 60 degrees C for 30 min with 5 min of shear was chosen as the best condition to dissolve MPC powders. The samples were also characterized for composition, presence of protein aggregates, and ratio of calcium to protein. The total calcium present in MPC increased with increasing concentration of protein; however, the total calcium-to-protein ratio was lower in MPC90 than in MPC56 and MPC70. The level of whey protein denaturation, the presence of kappa-casein-whey protein aggregates in the supernatant after centrifugation, and the amount of caseins dissociated from the micelle increased as the protein concentration in the powder increased. The total amount of casein macropeptide released was lower in samples from powders with a higher protein concentration than for MPC56 or the skim milk control. The gelation behavior of reconstituted MPC was tested in systems dispersed in water (5% protein) as well as in systems dispersed in skim milk (6% protein). The gelation time of MPC dispersions was considerably lower and the gel modulus was higher than those of reconstituted skim milk with the same protein concentration. When MPC dispersions were dialyzed against skim milk, a significant decrease in the gelation time and modulus were shown, with a complete loss of gelling functionality in MPC90 dispersed in water. This demonstrated that the ionic equilibrium was key to the functionality of MPC.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".