Effect of high intensity ultrasound on transglutaminase-catalyzed soy protein isolate cold set gel
Bibliographic record
Abstract
The effects of high intensity ultrasound (HIU, 105-110 W/cm(2) for 5 or 40 min) pre-treatment of soy protein isolate (SPI) on the physicochemical properties of ensuing transglutaminase-catalyzed soy protein isolate cold set gel (TSCG) were investigated in this study. The gel strength of TSCG increased remarkably from 34.5 to 207.1 g for TSCG produced from SPI with 40 min HIU pre-treatment. Moreover, gel yield and water holding capacity also increased after HIU pre-treatments. Scanning electron microscopy showed that HIU of SPI resulted in a more uniform and denser microstructure of TSCG. The content of free sulfhydryl (SH) groups was higher in HIU TSCG than non-HIU TSG, even though greater decrease of the SH groups present in HIU treated SPI was observed when the TSCG was formed, suggesting the involvement of disulfide bonds in gel formation. Protein solubility of TSCG in both denaturing and non-denaturing solvents was higher after HIU pretreatment, and changes in hydrophobic amino acid residues as well as in polypeptide backbone conformation and secondary structure of TSCG were demonstrated by Raman spectroscopy. These results suggest that increased inter-molecular ε-(γ-glutamyl) lysine isopeptide bonds, disulfide bonds and hydrophobic interactions might have contributed to the HIU TSCG gel network. In conclusion, HIU changed physicochemical and structural properties of SPI, producing better substrates for TGase. The resulting TSCG network structure was formed with greater involvement of covalent and non-covalent interactions between SPI molecules and aggregates than in the TSCG from non-HIU SPI.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".