Impact of Ultrafiltration Membrane Material on Peptide Separation from a Snow Crab Byproduct Hydrolysate by Electrodialysis with Ultrafiltration Membranes
Bibliographic record
Abstract
Electrodialysis with ultrafiltration membrane (EDUF) is a technology based on the separation of molecules according to their charge and molecular mass. Some works have already successfully demonstrated the recovery of bioactive peptide fractions. However, the impact of ultrafiltration membrane (UFM) material, used in the EDUF system, on the peptide migration has never been studied. Consequently, the objectives of this work were (1) to evaluate the effect of two different UFM materials on the selective separation of peptides from a snow crab byproduct hydrolysate by electrodialysis with ultrafiltration membranes and (2) to determine the effect of UFM material on their potential fouling by peptides. It appeared that, after 6 h of EDUF separation using polyether sulfone (PES) and cellulose acetate (CA) UFM, peptides with low molecular weights ranging from 300 to 700 Da represented the most abundant population in the KCl1 (compartment located near the anode for the recovery of anionic/acid peptide fractions) and KCl2 (compartment located near the cathode for the recovery of cationic/basic peptide fractions) permeates. Peptides with molecular weights ranging from 700 to 900 Da did not migrate during the EDUF treatment. Moreover, only CA UFM allowed the recovery of high molecular weight molecules (900-20000 Da) in both KCl compartments. Peptides desorbed from PES and CA UFM after 6 h of EDUF separation had low molecular weights and belonged mainly to the 600-700 Da molecular weight range. These peptides represented a low proportion of the peptides initially present in the snow crab byproduct hydrolysate with individual molecular weight range proportions from 1.52 ± 0.31 to 10.2 ± 2.32%.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".