Membrane Processes and Devices for Separation of Bioactive Peptides
Bibliographic record
Abstract
In recent years, functional foods and nutraceuticals has attracted much attention, particularly for their impact on human health and prevention of certain diseases. Consequently, the production and properties of bioactive peptides has received an increasing scientific interest over the past few years. Considering that most functional peptides are present in complex matrices containing a large number of hydrolyzed protein fractions, their separation and purification are required. Conventional pressure-driven processes can be used for amino acids and peptides separation but are limited by their fouling problems and their low selectivity when separating similar sized biomolecules. To improve the separation efficiency, an external electric field was applied during pressure-driven filtration. However, the pressure gradient brings about the accumulation of peptides at the nearby membrane surface and affects the membrane transport selectivity. Processes combining an electrical field as a driving force to porous membranes have been developed for the separation of biopeptides to obtain better purified products. Compounds of higher molecular weights than the membrane cut-off can be separated. The first trials were carried-out to perform the separation of amino acids and peptides with a filtration module specially designed and using one ultrafiltration membrane. More recently, electrodialysis with ultrafiltration membranes has been developed to fractionate simultaneously acidic and basic peptides, using a conventional electrodialysis cell, in which some ion exchange membranes are replaced by ultrafiltration ones. The perspectives in this field will be the understanding of the interactions of peptides and membrane as well as the development of new membrane materials limitating or increasing these interactions to improve the selectivity and the yield of production of specific peptides. This review article also discusses recent patents related to bioactive peptides.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".