Effect of Buttermilk Made from Creams with Different Heat Treatment Histories on Properties of Rennet Gels and Model Cheeses
Bibliographic record
Abstract
Although many studies have reported negative effects on cheese properties resulting from the use of buttermilk in cheese milk, the cause of these effects has not been determined. In this study, buttermilk was manufactured from raw cream and pasteurized cream, as well as from a cream derived from pasteurized whole milk. Skim milks with the same heat treatments were also manufactured to be used as controls. Compositional analysis of the buttermilks revealed a pH 4.6-insoluble protein content approximately 10% lower than that of the skim milk counterparts. Milk fat globule membrane (MFGM) proteins remained soluble at pH 4.6 in raw cream buttermilk; however, when heat was applied to cream or whole milk before butter making, MFGM proteins precipitated with the caseins. Rennet gel characterization showed that MFGM material in the buttermilks decreased the firmness and increased the set-to-cut time of rennet gels, but this effect was amplified when pasteurized cream buttermilk was added to cheese milk. The microstructure of gels was studied, and it was observed that gel appearance was very different when pasteurized cream buttermilk was used, as opposed to raw cream buttermilk. Model cheeses manufactured with buttermilks tended to have a higher moisture content than cheeses made with skim milks, explaining the higher yields obtained with buttermilk. Superior retention of MFGM particles was observed in model cheeses made from pasteurized cream buttermilk compared with raw cream buttermilk. The results from this study show that pasteurization of cream and of whole milk modifies the surface of MFGM particles, and this may explain why buttermilk has poor coagulation properties and therefore yields rennet gels with texture defects.
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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) | 0.000 | 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) | 0.001 | 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".