Acid Gelation in Heated and Unheated Milks: Interactions between Serum Protein Complexes and the Surfaces of Casein Micelles
Bibliographic record
Abstract
The acid-induced interactions between different protein particles in milk (casein micelles and serum protein/kappa-casein complexes) were studied in a series of different mixtures of heated and unheated proteins using diffusing wave spectroscopy (DWS) and small deformation rheology. The measurements were made as functions of pH during acidification by addition of glucono-delta-lactone (GDL). Heat treatment (85 degrees C, 10 min) affected the composition of the serum and the reactivity of casein micellar surface based on the pH at which the casein micelles aggregated during acidification. It was observed that the gel points as defined by DWS and rheology did not always coincide. The experiments showed that all systems containing heated serum proteins gelled at a higher pH than those containing unheated serum proteins. For systems containing heated micelles, an intermediate network can be formed between heat-induced aggregates of serum proteins and kappa-casein formed at the surfaces of the micelles and dispersed as soluble complexes in the serum. This can explain the observation that DWS measurements detected aggregation of casein micelles at an earlier stage than did rheology. For systems containing unheated micelles and soluble complexes from heated milk, the results appear to be explained only by a direct interaction between soluble serum protein complexes and the casein micelles themselves, once the pH has decreased to below about 5.5. Comparison of the different systems studied gives a more complete description of the possible mechanism of interaction of the different protein materials during the acid-induced coagulation of milk-based systems.
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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) | 0.000 | 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 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".