Scale-Up and Long-Term Study of Electrodialysis with Ultrafiltration Membrane for the Separation of a Herring Milt Hydrolysate
Bibliographic record
Abstract
Electrodialysis with ultrafiltration membrane (EDUF) was selected to separate a herring milt hydrolysate (HMH) in a scale-up and long-term study for the recovery of bioactive peptides. The scale-up was performed to maximise peptide recovery by placing a total membrane area of 0.08 m2 for each anionic and cationic compartment. Twelve consecutive runs were carried out, for a total of 69 h, with minimal salt solution cleaning in between experiments. The final peptide migration rate showed that cationic peptides had a higher average migration rate (5.2 ± 0.8 g/m2·h), compared to anionic peptides (4.7 ± 1.1 g/m2·h). Migration was also selective according to peptide identifications and molecular mass distribution where only small molecular weights were found (<1000 Da) in both recovery compartments. The areal system resistance slightly decreased during each run and the averaged values were stable in between experiments since they were all found in the 95% confidence interval. In addition, total relative energy consumption was quite consistent with an average value of 39.95 ± 6.47 Wh/g all along the 12 consecutive runs. Finally, according to membrane characterization, there was no visual fouling on the different membranes present in the EDUF cell after 69 h of treatment. This may be due to the salt cleaning in between experiments which allowed removal of peptides from the membranes, thus allowing recovering initial system working parameters at the beginning of each run. The entire process was revealed to be very consistent and repeatable in terms of peptide migration, global system resistance, and energy consumption. To the best of our knowledge, this is the first time such EDUF conditions (membrane surface, duration, and minimal salt cleaning between experiments) are being tested on a complex hydrolysate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 teacher head, 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".