Separation of Bioactive Peptides by Membrane Processes: Technologies and Devices
Bibliographic record
Abstract
Although many patents reported bioactive peptides with numerous demonstrated bioactivities and potential applications, there exist some limitations to the production of large quantities to satisfy the growing market demands. Indeed, considering that most functional peptides are present in complex matrices containing a large number of hydrolyzed protein fractions, their separation and purification are required. Some advances have been made in the use of conventional pressure-driven processes for the continuous production and separation of peptides, however, most of these patented technologies are not scalable and demonstrate a low selectivity when separating similar sized biomolecules. To improve the separation efficiency, the use of an external electric field during pressure-driven filtration was proposed and patented. However, whatever the claims, the pressure gradient brings about the accumulation of peptides at the nearby membrane surface and affects the membrane transport selectivity. To overcome these drawbacks, a recent patent proposed the simultaneous fractionation of acidic and basic peptides, using a conventional electrodialysis cell, in which some ion exchange membranes are replaced by ultrafiltration ones. The perspectives in the field of peptide separation will be the development of new membrane materials and new equipments such as microfluidic devices to improve selectivity and yield of production. Keywords: Bioactivity, bioactive peptide, concentration, devices, electrodialysis, electrophoresis, filtration membrane, ionexchange membranes, membrane processes, nanofiltration, nutraceuticals, patent, peptides, peptide sequence, purification process, separation, technology, ultrafiltration
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".