Protein Recovery in Soymilk and Various Soluble Fractions as a Function of Genotype Differences, Changes during Heating, and Homogenization
Bibliographic record
Abstract
Harovinton, a variety of tofu type soybean, and 11 derived null soybean genotypes lacking specific glycinin (11S) and beta-conglycinin (7S) protein subunits were investigated to determine whether changes in protein composition affected the protein recovery in soymilk and its soluble fractions after various centrifugation steps. As both heating and homogenization have a marked effect on the increase in protein solubility, the changes occurring during these processing steps were studied for each soybean genotype. Harovinton and 11S-null genotypes showed significantly higher protein yields than the other genotypes evaluated. Subunits of group I (A(1), A(2)) of glycinin had a negative impact on protein solubility in all treatments, but this effect was the greatest in unheated soymilk samples. Samples containing a high beta-conglycinin to glycinin ratio showed an effect of heating on the solubility of the protein, as beta-conglycinin subunits aggregate with heating. The presence of the alpha' subunit of beta-conglycinin aids in the recovery of protein in the supernatant prepared from lines containing group I (A(1,4) A(2)) glycinin. The results of this study will help determine which specific protein composition will confer an increased stability in soymilk and soymilk-derived products.
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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".