Soy protein recovery in a solvent‐free process using continuous liquid–solid circulating fluidized bed ion exchanger
Bibliographic record
Abstract
Soy protein concentrates and soy protein isolates act as ingredients in bakery, meat and dairy products, baby formulas, starting materials for spun textured vegetable products, and other nutritional supplements. In this study, the effectiveness of a liquid-solid circulating fluidized bed (LSCFB) ion exchanger is demonstrated for the recovery of soluble soy proteins from full fat and defatted soy flour. Under steady-state operating conditions, about 50% of the proteins could be recovered from the feed streams entering the ion exchanger. The LSCFB was shown to be a promising system for the recovery of soy protein from both defatted and full fat soy flour solutions. As the ion exchange process captures dissolved proteins, the system may offer a less damaging form of processing compared with the acid precipitation process where soy protein aggregates form and functionality is affected. In addition, the LSCFB allows simultaneous adsorption and desorption of the proteins allowing for a continuous operation. No prefiltration of feed containing suspended particles is required as well, because fluidization is used in place of packed bed technology to improve on current ion exchange processes.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".