Protein Subunit Composition Effects on the Thermal Denaturation at Different Stages During the Soy Protein Isolate Processing and Gelation Profiles of Soy Protein Isolates
Bibliographic record
Abstract
Abstract This study focussed on the evaluation of thermal denaturation at three different stages during soy protein isolation and the effect of subunit composition on the formation of heat‐induced soy protein gels. Soy protein isolates (SPI) were prepared from 12 high protein lines, Harovinton variety and 11 derived null‐lines which lacked specific glycinin (11S) and β‐conglycinin (7S) protein subunits. Protein denaturation during SPI processing was monitored by differential scanning calorimetry (DSC). The results showed that hexane extraction of oil from soybean flours at 23 °C or 105 °C did cause changes in protein conformation. Rheological measurements showed that lines with different subunit compositions and 11S:7S ratio had distinctive gelation temperatures and resulted in gels with different network structures. All lines formed particulate gels at 11% protein. The 11S:7S ratio was not correlated to final stiffness, measured as the storage modulus G ′, of SPI gels. Lower gelation temperatures were usually observed for 7S‐rich lines. The absence of A3 and the combination of A1, A2 and A4 subunits of 11S fraction may suggest the formation of stiffer gels. A more detailed study of the frequency dependence of G ′ for the various networks formed also indicated that differences in subunit composition influenced the network structures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".