Identification of Fouling Occurring during Coupled Electrodialysis and Bipolar Membrane Electrodialysis Treatment for Tofu Whey Protein Recovery
Bibliographic record
Abstract
Tofu whey, a by-product of tofu production, is rich in nutrients such as proteins, minerals, fats, sugars and polyphenols. In a previous work, protein recovery from tofu whey was studied by using a coupled environmental process of ED + EDBM to valorize this by-product. This process allowed protein recovery by reducing the ionic strength of tofu whey during the ED process and acidifying the proteins to their isoelectric point during EDBM. However, membrane fouling was not investigated. The current study focuses on the fouling of membranes at each step of this ED and EDBM process. Despite a reduction in the membrane conductivities and some changes in the mineral composition of the membranes, no scaling was evident after three runs of the process with the same membranes. However, it appeared that the main fouling was due to the presence of isoflavones, the main polyphenols in tofu whey. Indeed, a higher concentration was observed on the AEMs, giving them a yellow coloration, while small amounts were found in the CEMs, and there were no traces on the BPMs. The glycosylated forms of isoflavones were present in higher concentrations than the aglycone forms, probably due to their high amounts of hydroxyl groups, which can interact with the membrane matrices. In addition, the higher concentration of isoflavones on the AEMs seems to be due to a combination of electrostatic interactions, hydrogen bonding, and π-π stacking, whereas only π-π stacking and hydrogen bonds were possible with the CEMs. To the best of our knowledge, this is the first study to investigate the potential fouling of BPMs by polyphenols, report the fouling of IEMs by isoflavones and propose potential interactions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".