Physical properties of pizza Mozzarella cheese manufactured under different cheese-making conditions
Bibliographic record
Abstract
The effect of manufacturing factors on the shreddability and meltability of pizza Mozzarella cheese was studied. Four experimental cheeses were produced with 2 concentrations of denatured whey protein added to milk (0 or 0.25%) and 2 renneting pH values (6.4 or 6.5). The cheeses were aged 8, 22, or 36d before testing. Shreddability was assessed by the presence of fines, size of the shreds, and adhesion to the blade after shredding at 4, 13, or 22°C. A semi-empirical method was developed to measure the matting behavior of shreds by simulating industrial bulk packaging. Rheological measurements were performed on cheeses with and without a premelting treatment to assess melt and postmelt cheese physical properties. Lowering the pH of milk at renneting and aging the cheeses generally decreased the fines production during shredding. Adding whey protein to the cheeses also altered the fines production, but the effect varied depending on the renneting and aging conditions. The shred size distribution, adhesion to the blade, and matting behavior of the cheeses were adversely affected by increased temperature at shredding. The melting profiles obtained by rheological measurements showed that better meltability can be achieved by lowering the pH of milk at renneting or aging the cheese. The premelted cheeses were found to be softer at low temperatures (<40°C) and harder at high temperatures (>50°C) compared with the cheeses that had not undergone the premelting treatment. Understanding and controlling milk standardization, curd acidification, and cheese aging are essential for the production of Mozzarella cheese with desirable shreddability and meltability.
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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 | 0.000 | 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 | 0.001 |
| 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".