Semi-Industrial Production of a DPP-IV and ACE Inhibitory Peptide Fraction from Whey Protein Concentrate Hydrolysate by Electrodialysis with Ultrafiltration Membrane
Bibliographic record
Abstract
The separation by electrodialysis with ultrafiltration membranes (EDUF), at a semi-industrial scale, of a new whey protein hydrolysate obtained from a whey protein concentrate was assessed. After 6 h of treatment, more than 9 g of peptides were recovered in the peptide recovery fraction, for a recovery yield of 5.46 ± 0.56% and containing 18 major components. Among these components, positively charged peptides, such as ALPMHIR + PHMIR, LIVTQTMK and TKIPAVF, were present, and their relative abundances increased by nearly 1.25 X and up to 7.55 X. The presence of these peptides may be promising, as ALPMHIR has a strong activity against angiotensin-converting enzyme (ACE), and LIVTQTMK has structural properties that could interfere with dipeptidyl peptidase-IV (DPP-IV). Many neutral peptides were also recovered alongside those. Nevertheless, the inhibitory activity against DPP-IV and ACE increased from 2 X and 4 X, respectively, in the peptide recovery fraction compared to the initial hydrolysate, due to the improved content in bioactive peptides. Thus, this new hydrolysate is well-suited for the large-scale production of a peptide fraction with high bioactivities. Furthermore, what was achieved in this work came close to what could be achieved for the industrial production of a bioactive peptide fraction from whey proteins.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".