Influence of Homogenization Conditions of Buffalo Milk on the Recovery of Milk Constituents and Yield of Mozzarella Cheese
Bibliographic record
Abstract
Mozzarella cheese making involves losses of milk constituents, especially during plasticizing stage of cheese curd. Buffalo milk is considered more suitable than cow milk for Mozzarella cheese making, especially in terms of colour, yield and stretch property of resultant product. Homogenization of milk reduces the losses of milk constituents, increases its whiteness and is expected to render superior flavor to cheese. The fat globule size for buffalo milk is larger and the cheese tends to be firmer and chewy as compared to cow milk counterpart. Homogenization of buffalo milk is of significance in this regard since it can improve the color, recovery of milk constituents culminating in higher cheese yield, a mellower product with lower tendency to oil-off during baking applications. Since the conditions of homogenization affects the recovery of milk constituents, it was decided to study temperature and pressure of homogenization on such aspect including cheese yield. Homogenization of standardized buffalo milk at 55 or 65oC and 4.90 MPa (P2) pressure is found beneficial with regard to recovery of milk fat, while use of lower pressure i.e. 2.45 MPa (P1) at above temperatures is found beneficial for protein and TS recoveries. P2 pressure is more beneficial than P1 pressure in improving the fat recovery in buffalo milk Mozzarella cheese. There is an improvement in the yield of Mozzarella cheese with an increase in homogenization pressure. The yield of Mozzarella cheese prepared using buffalo milk homogenized at P2 and P1 pressure (at 65oC) was 17.00% and 16.10% respectively. The recoveries of milk fat, protein and TS and per cent yield for control cheese was 83.68%, 84.10%, 56.74% and 14.53% respectively.
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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 | 0.000 |
| 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".