Effect of soluble calcium on the renneting properties of casein micelles as measured by rheology and diffusing wave spectroscopy
Bibliographic record
Abstract
Addition of calcium chloride to milk has positive effects on cheese-making because it decreases coagulation time, creates firmer gels, and increases curd yield. Although addition of calcium chloride is a widely used industrial practice, the effect of soluble calcium on the preliminary stages of gelation is not fully understood. In addition, it is not known whether the manner of addition and equilibration of the soluble calcium would affect the rennetability of the casein micelles. Therefore, the aim of this paper was to study the details of the coagulation behavior of casein micelles in the presence of additional calcium, and to elucidate whether the manner in which this cation is added (directly as calcium chloride or by gradual exchange through dialysis) affects the functionality of the micelles. Calcium was added as CaCl(2) (1 mM final added concentration) directly to skim milk or indirectly using dialysis against 50 volumes of milk. Additional soluble calcium did not affect the primary phase of the renneting reaction, as demonstrated by the analysis of the casein macropeptide (CMP) released in solution; however, it shortened the coagulation time of the micelles and increased the firmness of the gel. The turbidity parameter of samples with or without calcium showed that similar amounts of CMP were needed for particle interactions to commence. However, the amount of CMP released at the point of gelation, as indicated by rheology, was lesser for samples with added calcium, which can be attributed to a greater extent of calcium bridging on the surface or between micelles. The results also showed that the manner in which calcium was presented to the micelles did not influence the mechanism of gelation.
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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) | 0.000 | 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) | 0.001 | 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".