Invited review: Understanding the behavior of caseins in milk concentrates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The colloidal properties of the casein micelles play a major role in the structural properties of milk protein concentrates. Because of their great technological importance, the structural-functional relationships of casein micelles have been studied for decades in skim milk; however, novel ingredients are now available with higher protein concentrations and varying in composition. The colloidal behavior of caseins in these systems is not fully understood. Concentrates prepared with membrane technologies, and subjected to pre- or post-modifications that affect their technological functionality, have become increasingly widespread. This has created large opportunities for innovation and generation of value-added ingredients. The manner in which caseins interact with themselves and the other components in these concentrates will affect the structure of the final matrix. During concentration by filtration, the interparticle distance between the micelles decreases considerably, increasing their spatial correlation and decreasing their diffusivity. Rearrangements occur due to changes in environmental conditions, such as ionic composition, osmotic stress, shear, pH, or heating temperature. This will have important consequences on bulk viscosity of the concentrates, as well as on the mode of formation of structures' building blocks. This paper aims at highlighting some of the important factors affecting the colloidal structure of casein micelles, their destabilization and network formation, namely, processing history, volume fraction, composition of the serum phase, and ionic equilibrium. Understanding these factors will lead to a better quality control of dairy ingredients and to the development of a new generation of ingredients with targeted functionality.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it