Dynamic covalent interactions between baicalin and soybean protein: Effect of the baicalin on structure, antioxidant properties, and emulsion stability of soybean protein conjugate
Bibliographic record
Abstract
To improve the emulsion properties of soybean protein isolate (SPI), the effect of baicalin (BC) concentrations on the structure, antioxidant properties, and emulsion stability of SPI-BC conjugates were investigated. Structure analysis revealed that BC induced structural depolymerization and unfolding of SPI through the formation of covalent bonds (CN and CS), increasing hydrophilicity, stabilizing the interface, and enabling the formation of smaller oil droplets. The increase in zeta potential of SPI-BC conjugates enhanced electrostatic repulsion between droplets, preventing aggregation, while the strengthened interfacial protein network improved viscosity, shear stress, and thixotropic recovery, thereby significantly stabilizing the emulsion structure. The addition of 0.4 mg/mL BC exhibited the optimal emulsification performance, increasing emulsion stability by 35.4 %. Moreover, SPI-BC emulsions demonstrated enhanced stability against various environmental stressors (storage, heating, pH, ionic strength, and oxidation). These findings confirmed that BC covalent modification enhanced the structural properties of SPI and improved the stability of protein-based emulsions through structural regulation. This study provides new insights into the potential of BC in improving SPI functionality.
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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".