Charge Compensation, Phase Diagram, and Protein Aggregation in Soy Protein–Gum Arabic Complex Formation
Bibliographic record
Abstract
Mixtures of soy protein (SP) and gum arabic (GA) formed an electrostatic complex in a relatively narrow pH range at very low ionic strength. The conditions under which the complexes could be formed were determined using turbidimetric measurements first. In salt-free condition and 1:1 SP/GA mixture, critical pH values with the formation of soluble (pH c = 4.40), insoluble (pH φ1 = 3.55), and maximum (pH opt = 3.15) complexes were observed. As SP/GA ratios increased from 1:4 to 8:1, critical pH values shifted toward higher pH. Charge densities (ZN) for SP and GA were calculated from electrophoretic mobility using soft particle analysis theory. Results showed that a 1:1 charge ratio at pH φ1 was found at any SP/GA ratio, indicating that charge compensation was fulfilled for SP/GA insoluble complex formation. A SP–GA–water ternary phase diagram was built at pH 4.0. The influence of the total biopolymer concentration (0–10% w/w) and SP/GA ratio was represented in the phase diagram. At a total concentration of 0.10%, results were consistent with the turbidity measurement; that is, no phase separation occurred at an SP/GA ratio lower than 1:2 at pH 4.0. Salt effect (NaCl, 0–500 mmol/L) on SP/GA complexes was discussed. Results indicated that SP/GA complexing, which led to the formation of turbidity peaks at pH 3.2, was suppressed when NaCl concentrations were ≥50 mmol/L, whereas the remarkable increase in turbidity around pH 5.0 was caused by the aggregation of soy protein molecules on which gum arabic could be adsorbed.
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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 | 0.000 |
| 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".