Effect of partial whey protein depletion during membrane filtration on thermal stability of milk concentrates
Bibliographic record
Abstract
Membrane filtration technologies are widespread unit operations in the dairy industry, often employed to obtain ingredients with tailored processing functionalities. The objective of this work was to better understand the effect of partial removal of whey proteins by microfiltration (MF) on the heat stability of the fresh concentrates. The micellar casein concentrates were compared with control concentrates obtained using ultrafiltration (UF). Pasteurized milk was microfiltered (80 kDa polysulfone membrane) or ultrafiltered (30 kDa cellulose membrane) without diafiltration (i.e., no addition of water) to 2× and 4× concentration, based on volume reduction. The final concentrates showed no differences in pH, casein micelle size, or mineral concentration in the serum phase. The micellar casein retentates (obtained by MF) showed a 20 and 40% decrease in whey protein concentration compared with the corresponding UF milk protein concentrates for 2× and 4× concentration, respectively. The heat coagulation time decreased with increasing protein concentration, regardless of the treatment; however, MF retentates showed a higher thermal stability than the corresponding UF controls. The average diameter for casein micelles increased after heating in UF but not MF concentrates. The turbidity (measured by light scattering) increased after heating, but to a higher extent for UF retentates than for MF retentates at the same protein concentration. It was concluded that the reduced amount of whey protein in the MF retentates caused a significant increase in the heat stability compared with the corresponding UF retentates. This difference was not due to ionic composition differences or pH, but to the type and amount of complexes formed in the serum phase.
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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 | 0.003 | 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 | 0.001 |
| 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".