Assessment of the Performance of Electrodialysis in the Removal of the Most Potent Odor-Active Compounds of Herring Milt Hydrolysate: Focus on Ion-Exchange Membrane Fouling and Water Dissociation as Limiting Process Conditions
Bibliographic record
Abstract
Herring milt hydrolysate (HMH), like many fish products, presents the drawback to be associated with off-flavors. As odor is an important criterion, an effective deodorization method targeting the volatile compounds responsible for off-flavors needs to be developed. The potential of electrodialysis (ED) to remove the 15 volatile compounds identified, in the first part of this work, for their main contribution to the odor of HMH, as well as trimethylamine, dimethylamine and trimethylamine oxide, was assessed by testing the impact of both hydrolysate pH (4 and 7) and current conditions (no current vs. current applied). The ED performance was compared with that of a deaerator by assessing three hydrolysate pH values (4, 7 and 10). The initial pH of HMH had a huge impact on the targeted compounds, while ED had no effect. The fouling formation, resulting from electrostatic and hydrophobic interactions between HMH constituents and ion-exchange membranes (IEM); the occurrence of water dissociation on IEM interfaces, due to the reaching of the limiting current density; and the presence of water dissociation catalyzers were considered as the major limiting process conditions. The deaerator treatment on hydrolysate at pH 7 and its alkalization until pH 10 led to the best removal of odorant compounds.
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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".