Impact of Preheating Temperature on the Separation of Whey Proteins When Combined with Chemical or Bipolar Membrane Electrochemical Acidification
Bibliographic record
Abstract
Separation of α-lactalbumin and β-lactoglobulin improves their respective nutritional and functional properties. One strategy to improve their fractionation is to modify their pH and ionic strength to induce the selective aggregation and precipitation of one of the proteins of interest. Electrodialysis with bipolar membrane (EDBM) is a green process that simultaneously provides acidification and demineralization of a solution without adding any chemical compounds. This research presents the impact on whey proteins separation of different preheating temperatures (20, 50, 55 and 60 °C) combined with EDBM or chemical acidification of 10% whey protein isolate solutions. A β-lactoglobulin fraction at 81.8% purity was obtained in the precipitate after EDBM acidification and preheated at 60 °C, representing a recovery yield of 35.8%. In comparison, chemical acidification combined with a 60 °C preheating treatment provides a β-lactoglobulin fraction at 70.9% purity with a 11.6% recovery yield. The combination of EDBM acidification with a preheating treatment at 60 °C led to a better separation of the main whey proteins than chemical acidification.
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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) | 0.001 | 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".