Effect of added salt and increase in ionic strength on skim milk electroacidification performances
Bibliographic record
Abstract
Bipolar-memibrane electroacidification (BMEA) technology which uses the property of bipolar membranes to split water and the demineralization action of cation-exchange membranes (CEM), was tested for the production of acid casein. BMEA has numerous advantages in comparison with conventional isoelectric precipitation processes of proteins used in the dairy industry. BMEA uses electricity to generate the desired ionic species to acidify the treated solutions. The process can be precisely controlled, as electro-acidification rate is regulated by the effective current density in the cell. Water dissociation at the bipolar membrane interface is continuous and avoids local excess of acid. In-situ generation of dangerous chemicals (acids and bases) reduces the risks associated with the handling, transportation, use and elimination of these products. The aim of this study was to evaluate the performance of BMEA in different conditions of added ionic strength (p(added) = 0, 0.25, 0.5 and 1.0 M) and added salt (CaCl2, NaCl and KCl). The combination of KCl and p(added) = 0.5 M gave the best results with a 45% decrease in energy consumption. The increased energy efficiency was the result of a decrease in the anode/cathode voltage difference. This was due to an increase of conductivity, produced by addition of salt, necessary to compensate for the lack of sufficiently mobile ions in the skim milk. However, the addition of salts, irrespective of type or ionic strength, increased the required operation time. The protein profile of isolates were similar under all experimental conditions, except at 1.0 M-CaCl2.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".