Diafiltration affects the gelation properties of concentrated casein micelle suspensions obtained by filtration
Bibliographic record
Abstract
Using membrane filtration it is possible to selectively concentrate proteins and, in the case of microfiltration, concentrate casein micelles. During filtration, water is often added and this practice, called diafiltration, causes further release of permeable components and maintains filtration efficiency. Filtration causes changes in composition of the protein as well as the soluble phase, including soluble calcium, which is a critical factor controlling the gelation properties of the casein micelles in milk. It was hypothesized that concentrates obtained using membrane filtration with or without diafiltration would have different gelation behavior. To test this hypothesis, two concentrates of similar casein micelle volume fraction were prepared, using spiral wound polymeric microfiltration membranes with a 800 kDa molecular weight cutoff, with or without diafiltration. The concentrates showed a gelation behavior comparable to that of skim milk, with a similar gelation time and with a higher firmness, due to the higher number of protein linkages in the network. In contrast, the hydrolysis of κ-casein by chymosin and casein aggregation were inhibited in diafiltered casein micelle suspensions. When the concentrates were recombined with the original skim milk to a final concentration of 5% protein, which re-established a similar soluble phase composition, differences in gelation behavior were no longer observed: both treatments showed similar gelation time and gel firmness. These results confirmed that membrane filtration can result in concentrates with different functionality, and that ionic environmental conditions are critical to the aggregation behavior of casein micelles. This is of particular significance in industrial settings where these fractions are used as a way to standardize proteins in cheese making.
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 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 | 0.001 | 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 teacher head, 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".