Production of highly purified fractions of α-lactalbumin and β-lactoglobulin from cheese whey using high hydrostatic pressure
Bibliographic record
Abstract
Despite extensive research on the topic, valorization of dairy by-products remains challenging. Cheese whey is of particular interest because it contains valuable proteins such as α-lactalbumin (α-LA) and β-lactoglobulin (β-LG). However, selective fractionation of these 2 proteins into pure fractions is complex because of their similar molecular weights. In this study, we proposed an innovative protein separation strategy based on coupling high hydrostatic pressure (HHP) with acidification of whey at pH 4.6. We investigated the effect of single-cycle HHP (600 MPa) for 5, 10, and 15 min and multiple-cycle HHP (1-3 cycles of 5 min at 600 MPa) on α-LA and β-LG fractionation from cheese whey at initial pH (control, pH 6.66) and acidified to pH 4.6. All pressurization conditions with acidified whey induced a drastic aggregation of β-LG compared with control whey. The highest degrees of purification (75 and 98%, respectively) and yields (95 and 88%, respectively) of α-LA and β-LG were obtained with the application of single-cycle HHP treatment of acidified whey at pH 4.6 at 600 MPa for 5 min. Our results showed the strong potential of using HHP as an innovative tool for the fractionation of valuable proteins such as α-LA from cheese whey.
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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 | 0.000 |
| 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) | 0.000 | 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".