Production of calcium- and magnesium-enriched caseins and caseinates by an ecofriendly technology
Bibliographic record
Abstract
Finding new environmentally friendly ways of producing proteins has never been of such critical public interest, both to meet consumers' needs and to preserve the environment. Milk proteins are among the most attractive protein types due to their high nutritional value and attractive functional properties. In this work, the separation of caseins by conventional chemical acidification was compared with electrodialysis with bipolar membrane coupled to an ultrafiltration module (EDBM-UF), a green process that allows the precipitation of caseins by H + generated in situ by the bipolar membrane and, simultaneously, the production of a separated NaOH stream from OH -electrogenerated by the bipolar membrane. Caseinate production using this NaOH stream by-product and the quantity of NaOH needed to produce caseinates from both methods were also investigated. Hence, the purity and composition of caseins and caseinates were compared in terms of protein, ash, and lactose contents as well as mineral composition. The results showed for the first time that caseinates can be produced by solubilizing caseins with NaOH stream from the EDBM process. Furthermore, the caseins and caseinates produced by EDBM-UF were equivalent in terms of lactose and protein contents to their respective caseins and caseinates that were chemically produced but presented slightly lower sodium content and 3 to 4 times higher magnesium and calcium contents. The fact that calcium and magnesium are likely bound to milk caseins would ensure their favorable absorbability. These caseins or caseinates from the new EDBM-UF process could be suitable as an improved protein-based calcium or magnesium supplement, both for their enhanced nutritional quality and because they are produced by a "green" process.
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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 | 0.001 | 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 | 0.001 |
| 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".