Investigation of the Colloidal Interactions at Play in Combined Acidification and Rennet of Different Heat-Treated Milks
Bibliographic record
Abstract
The properties of gels obtained by combined acidification and rennet were investigated for milks heated at different temperature-time regimes using a high temperature short time (HTST) pilot plant system. Increasing amounts of heat-induced whey protein complexes were found in the soluble phase as a function of heating time/temperature, and only in the most extensively heated milk (i.e., 85 degrees C/300 s), these complexes were in quantities comparable to those reported in previous studies. Two levels of rennet were studied, and at the gelation pH, the amount of CMP released was 11 +/- 1% and 26 +/- 5% in the low and high rennet experiments, respectively. These two levels of rennet caused profound changes in the gelation behavior and in the structure development of the network. When a small amount of rennet was used, different heating temperature-time regimes did not affect the first stage of renneting. Increasing the extent of milk heat treatment and/or the level of rennet increased the pH of gelation and the stiffness of the gels. This work is the first to compare the effect of heating (using a pilot plant setup) and amount of rennet on the destabilization and interaction of casein micelles during aggregation by combined rennet and acidification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| 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".