Composition and Functional Properties of Soy Protein Isolates Prepared Using Alternative Defatting and Extraction Procedures
Bibliographic record
Abstract
ABSTRACT: In this study, the effects of alcohol defatting using ethanol, methanol, and non‐alcoholic aqueous extraction methods on the yield, purity, and functionality of soy protein isolates were investigated. Soy protein extraction conditions were also modified (heat and mild acidic treatment before protein alkaline extraction, heat isoelectric precipitation, and non‐neutral resolubilization of proteins), and the effects on the isolate properties were evaluated. Results showed that ethanol and aqueous extraction were potential alternatives to hexane. The soy protein isolates (SPI) obtained from these samples had protein contents of more than 90% and 84%, respectively, with functional properties comparable to those of SPI prepared from hexane defatted meal. Major differences were a decrease in the emulsifying activity properties of the SPIs resulting from the alternative defatting techniques, with, however, improved emulsion stability and foaming properties for the aqueous extracted SPIs. A marked decrease in the fat‐holding capacity of the SPI made from methanol defatted meal was also noted. Modifying the protein isolation procedure also greatly influenced the functional properties of soy protein isolates. The results of the present investigation demonstrate that soy processing conditions can be modified to obtain soy proteins ingredients with specific functional properties.
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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) | 0.000 | 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) | 0.001 | 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".