Pasteurization Affects Aggregation of Acidified Milk Dispersions and Pectin
Bibliographic record
Abstract
Commercial pectins were added at stabilizing levels to acidified milk dispersions and the effect of pectin type and pasteurization on the pectin-protein interaction was evaluated by gel permeation chromatography with in-line multi-angle light scattering, refractive index, and UV detectors. Commercial pectins had no apparent effect on the differences in molecular weight distributions of the soluble material in the dispersions. The weight average molecular weight (Mw) of individual pectins were 1.2 to over 4 million and were polydisperse with Mw/Mn ratios greater than 1.0. The Mw of soluble components in the mixed dispersions were 2-8 times higher than pectin alone and had greater polydispersity. Pasteurization had a greater effect on molecular weight distributions and polydispersity than pectin type. A second, high molecular weight aggregate was observed in all pasteurized samples. This aggregate was ≈ 10 times larger after pasteurization compared to non-pasteurized samples.
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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 | 0.000 | 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) | 0.000 | 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".