Influence of Heating on Oil-in-Water Emulsions Prepared with Soybean Soluble Polysaccharide
Bibliographic record
Abstract
The effect of heating on the physicochemical properties of emulsions prepared with soybean soluble polysaccharide (SSPS) was investigated. The emulsions were stable after heating at 90 degrees C for up to 30 min. Heating at different pH values or in the presence of CaCl2 (<10 mM) did not affect the stability; however, at higher concentrations of calcium ions, the emulsion particle size increased. Two fractions, a high molecular weight (HMF) and a low molecular weight (LMF) fraction, were separated from the crude SSPS preparation by gel fitration. Emulsions prepared with SSPS/HMF (MW = 310-420 kDa) showed little change in size with heating, while the protein impurities of the SSPS/LMF fraction formed aggregates by heating at pH 7. Analysis of the heat-induced aggregation of the two fractions of SSPS suggested that the changes in SSPS functionality with heating can be attributed to the protein impurities (LMF) present in the SSPS.
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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) | 0.001 | 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) | 0.001 | 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".