Electrodialytic Phenomena and Their Applications in the Dairy Industry: A Review
Bibliographic record
Abstract
Electrodialysis (ED) is an electrochemical separation process by which electrically-charged species are transported from one solution to another. ED is a combined method of dialysis and electrolysis and can be performed with two main cell types: multi-membrane cells for dilution-concentration and water dissociation applications (membrane phenomena), and electrolysis cells for redox reactions (electrode phenomena). The dilution-concentration principle applications in the dairy industry consist mainly of the demineralization of milk or milk by-products. The use of ED with monopolar membrane for protein separation and acid caseinate production, and in bioreactors for organic acid production, is also studied in the dairy industry. The interest of ED as a membrane process has been triggered recently by the development of a new membrane type, bipolar membrane. This membrane carries out the dissociation of water molecules. ED with bipolar membranes was applied very recently to the production of lactic acid from whey product fermentation, production of caseinates, and fractionation of whey proteins. Two principle applications of electrode reactions were published: electrochemical coagulation (EC) to precipitate milk proteins, and electroreduction for the reduction of disulfide bonds in the proteins. It appears in this article that processes using membrane phenomena are more numerous and developed than electrolytic applications. This is the composition of milk and the lack of knowledge of redox reactions of the different food compounds that limit the applications and the development of electrolytic phenomena. Electrodialytic phenomena present a great potential for application in the dairy industry, and more generally, in the food industry; many of these applications have to be discovered.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".