Effect of Dynamic High Pressure Homogenization on the Aggregation State of Soy Protein
Bibliographic record
Abstract
Although soy proteins are often employed as functional ingredients in oil-water emulsions, very little is known about the aggregation state of the proteins in solution and whether any changes occur to soy protein dispersions during homogenization. The effect of dynamic high pressure homogenization on the aggregation state of the proteins was investigated using microdifferential scanning calorimetry and high performance size exclusion chromatography coupled with multiangle laser light scattering. Soy protein isolates as well as glycinin and beta-conglycinin fractions were prepared from defatted soy flakes and redispersed in 50 mM sodium phosphate buffer at pH 7.4. The dispersions were then subjected to homogenization at two different pressures, 26 and 65 MPa. The results demonstrated that dynamic high pressure homogenization causes changes in the supramolecular structure of the soy proteins. Both beta-conglycinin and glycinin samples had an increased temperature of denaturation after homogenization. The chromatographic elution profile showed a reduction in the aggregate concentration with homogenization pressure for beta-conglycinin and an increase in the size of the soluble aggregates for glycinin and soy protein isolate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".