Effect of Glycosylation under Wet-Heating Condition on Functional Properties of Soybean Protein Isolate
Bibliographic record
Abstract
The effect of glycosylation under wet-heating conditions on functional properties of soybean protein isolate(SPI) was investigated by determining the pH, solubility and gel propert ies of glycosylated products from the reaction between 8 g/100 mL of SPI and glucose with a weight ratio of 4:1 in double distilled water at 70, 80 and 90 ℃ for 0, 1, 2, 3, 4, 5 and 6 h, respectively. The results showed that the color of the model system at each temperature became darker with extended heating time, the pH was decreased, the sobubility, emulsifying activity and emulsion stability were signifi cantly improved(P 0.05), and the gel springiness and hardness tended to initially increase and then decrease. The most obvious improvement was observed when the glycosylation reaction temperature was 90 ℃. The solubility and emulsifying activity were increased from 17.37% and 0.168 at 0 h to 38.7% and 0.574 at 6 h, and the highest gel hardness and springiness, 81.3 g and 0.936 were obtained at 3 h. Hence, glycosylated modifi cation can effectively improve functional properties of soy protein isolate.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".