Electrodialytic Phenomena and Their Applications in the Dairy Industry: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it