Carbohydrate and Mineral Removal during the Production of Low-Phytate Soy Protein Isolate by Combined Electroacidification and High Shear Tangential Flow Ultrafiltration
Bibliographic record
Abstract
In this work, soy protein isolates were produced by a combination of electroacidification and high shear tangential flow hollow fiber ultrafiltration with a 100 kDa membrane under constant pressure. The filtration performance was evaluated by comparing the filtration time and the final product composition for an electroacidified (pH 6) and a non-electroacidified (pH 9) soy protein extract. The removal of carbohydrates during the filtration was always consistent with the theoretical predictions (based on free permeability assumption) for both the electroacidified and the non-electroacidified feeds. A higher removal of calcium, magnesium, and phytic acid was achieved during the filtration of the electroacidified feed compared to the non-electroacidified feed. However, the electroacidification pretreatment had a negative impact on the permeate flux and resulted in more significant membrane fouling with correspondingly longer filtration times. A discontinuous diafiltration enhanced the removal of carbohydrates and minerals, thus yielding a product with higher protein content but was unable to improve the permeate flux for the electroacidified feed.
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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 | 0.000 |
| 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 | 0.000 | 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 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".