Selective separation of cationic peptides from a tryptic hydrolysate of β‐lactoglobulin by electrofiltration
Bibliographic record
Abstract
Electrofiltration (EF) was used to selectively separate cationic (basic) peptides contained in a tryptic beta-lactoglobulin (beta-LG) hydrolysate, with particular emphasis on the isolation of basic sequence beta-LG 142-148, which is a potential antihypertensive peptide. Both the influence of feed solution pH and operating parameters (transmembrane pressure, feed velocity) were assessed to find optimum conditions enabling the fractionation between peptides during EF. The cathode (-) was inserted in the permeate side to increase the separation of basic peptides contained in the tryptic beta-LG hydrolysate as compared to conventional NF. The highest separation factor between basic and neutral peptides was obtained at pH 9 using G-10 membrane with a molecular weight cut-off (MWCO) of 2,500 g/mol, at 5 V with the lowest transmembrane pressure (0.344 MPa) and feed velocity (0.047 m/s). The transmission behavior of the peptides during EF was better explained when taking into account the positive/negative charge ratio. Because of its 3+/1- charge ratio, beta-LG 142-148 had the highest transmission during EF. Consequently, its relative concentration was raised from 3.5% in the initial tryptic beta-LG hydrolysate up to 38% in the permeate. The electric field seemed more effective when the convective/shearing forces were minimized.
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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) | 0.000 | 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".