Optimization of Cheese Whey Ultrafltration/Diafltration for the Production of Beverage Liquid Protein Concentrates with Lactose Partially Removed
Bibliographic record
Abstract
The processing of cheese whey pre-concentrated by reverse osmosis is carried out through ultrafiltration in diafiltration mode to produce whey protein concentrates with lower lactose content to be incorporated in beverages. The initial cheese whey protein and lactose contents are 2.13g/100g and 13.22g/100g, respectively.The commercial membranes, GR95PP, supplied by Alfa Laval, Denmark, were characterized in terms of a hydraulic permeability of 1.21 l/(h∙m^2∙bar) and a molecular weight cut-off of 7500 Dalton. The permeation tests were carried out in a plate and frame Lab-Unit 20 from Alfa Laval, Denmark, and a membrane surface area of 0.072 m2 was installed.The ultrafiltration of cheese whey in total recirculation mode yielded two asymptotic variations of the permeate fluxes versus the transmembrane pressure. For operating pressures up to 12 bar the permeate flux increases linearly with the pressure. Then, with the increasing pressure, they deviate from linearity and reach a limiting flux of 8.79 l/(h∙m^2∙bar) at 30 bar. The slope of the asymptotic linear variation is 0.48 l/(h∙m^2∙bar). To have minimal effects of concentration polarization the operating pressure was set-up at 12 bar.The optimization of ultrafiltration/diafiltration was carried out in terms of the volumetric concentration factors and the frequency of diavolumes addition. At a volumetric concentration factor of 1.32 the lactose content decreased from 13.22% to 5.7%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".