Effects of Type of Added Salt and Ionic Strength on Physicochemical and Functional Properties of Casein Isolates Produced by Electroacidification
Bibliographic record
Abstract
A procedure developed for soybean protein precipitation which was based on electrodialysis was tested for the production of acid casein from reconstituted skim milk. In a previous paper, the performance of bipolar membrane electroacidification (BMEA) was evaluated under different conditions of ionic strength (micro(added) = 0, 0.25, 0.5, or 1.0 M) and added salt (CaCl(2), NaCl, or KCl) (1). The aim of this study, which is the complement of the work on evaluation of BMEA performance, was to evaluate the functionality of the protein isolates produced by BMEA and to compare the BMEA isolates to commercial isolates and an isolate produced by chemical acidification. It was not possible to show differences between the functional properties of isolates produced by BMEA, except at 1 M CaCl(2) micro(added), due to the variability of the isolates. However, the results showed that it is possible to obtain isolates similar to commercial isolates and that the addition of salt during the process does not induce variations in functional properties. From results on mineral concentrations, it appeared that the addition of monovalent cations did not influence the retention of monovalent or divalent cations in the BMEA isolates, while addition of divalent cations (CaCl(2)) influenced the retention of magnesium. According to previous results on evaluation of BMEA performances under different conditions of ionic strength and added salt, the difference observed for the BMEA isolate produced at 1.0 M CaCl(2) was confirmed.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".