Improved Antimicrobial Properties of White Wastewater Protein Hydrolysate Through Electrodialysis with an Ultrafiltration Membrane (EDUF)
Bibliographic record
Abstract
This study investigated white wastewater (WW) as a potential source of antimicrobial peptides, employing hydrolysis with Pronase E followed by separation through electrodialysis with ultrafiltration membranes (EDUF) to increase the value of dairy components within a circular economy framework. The WW hydrolysate was divided into two key fractions: the cationic recovery compartment (CRC) and the anionic recovery compartment (ARC). The EDUF process effectively separated peptides, with peptide migration rates reaching 6.83 ± 0.59 g/m2·h for CRC and 6.19 ± 0.66 g/m2·h for ARC. Furthermore, relative energy consumption (REC) increased from 1.15 Wh/g to 2.05 Wh/g over three hours, in line with trends observed in recent studies on electrodialysis energy use. Although 29 peptides were statistically selected from the CRC (20) and ARC (9) compartments, no antibacterial activity was exhibited against Clostridium tyrobutyricum and Pseudomonas aeruginosa; however, antifungal activity was observed in the feed and ARC compartments. Peptides from the ARC demonstrated activity against Mucor racemosus (MIC = 0.156 mg/mL) and showed selective antifungal effects against Penicillium commune (MIC = 0.156 mg/mL). This innovative approach paves the way for improving the recovery of anionic peptides through further optimization of the EDUF process. Future perspectives include synthesizing selected peptides and evaluating their antifungal efficacy against these and other microbial strains, offering exciting potential for applications in food preservation and beyond.
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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".