Advancing Faba Bean Protein Purification Using Membrane Technology: Current State and Future Perspectives
Bibliographic record
Abstract
Plant-based proteins are gaining popularity because of their appeal to vegetarians and vegans, alignment with scientific and regulatory recommendations, and the environmental impact associated with livestock production. Several techniques are employed for the separation, isolation, and purification of plant-based proteins including membrane-based separation, diafiltration, centrifugation, chromatography, electrophoresis, micellar precipitation, and isoelectric precipitation. Despite decades of application, these techniques still have some limitations such as scale-up challenges, high solvent consumption, chemical/biological disposal, and the possibility of protein loss during precipitation or elution. Membrane separation processes are the most effective purification/concentration technology in the production of plant-based protein isolates and concentrates due to their selective separation, simple operational conditions, and easy automation. Membrane separation processes yielded products with higher protein content compared to isoelectric precipitation, and all concentrates presented good functional properties with expected variability among different legumes. This review critically focuses on the membrane technology advances and challenges for the purification of plant-based protein isolates. This study also highlights the plant-based diet trend, the market, composition, and the protein isolate of the faba bean, in addition to the emerging technologies for the elimination of antinutritional compounds.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".