Addition of Pectin and Soy Soluble Polysaccharide Affects the Particle Size Distribution of Casein Suspensions Prepared from Acidified Skim Milk
Bibliographic record
Abstract
Pectins are negatively charged polysaccharides employed as stabilizers in acidified milk dispersions, where caseins aggregate because of the low pH and serum separation needs to be prevented. The objective of this research was to study the effect of charge on the stabilizing functionality of the polysaccharide in acid milk drinks. Unstandardized pectins with various charges (as degree of esterification, DE) as well as soybean soluble polysaccharide (SSPS) were tested for their stabilizing behavior as a function of pH and concentration. Skim milk was acidified by glucono-delta-lactone and then homogenized in the presence of polysaccharide at different pH values (in the range from 4.2 to 3.0). Measurements of particle size distribution demonstrated that pectins with a DE of 71.4, 68.6, and 67.4 stabilized milk at pH > 4.0. Pectins with a lower DE (63.9%) needed a higher concentration (0.4%) at the same pH to show a monomodal distribution of particle sizes. Pectins with lower DE (<50%) did not stabilize the dispersions. Although this difference in behavior was attributed mainly to the pectin charge, the efficiency in stabilizing the casein dispersion decreased with decreasing pectin size. For example, the high methoxyl pectin (HMP) with 63.9 DE was smaller in size than the HMPs with a higher charge. Pectins showed a pH-dependent stabilization effect, as at pH < 4.0 the dispersions contained aggregates. When SSPS was used to stabilize acid milk, at pH < 4.0, it showed a better stabilization behavior than HMP. When SSPS and pectin were used in combination, the particle size distribution of the acid milk dispersion was pH-dependent, and results were similar to those for samples containing pectin alone. This suggested that in the mixture, pectin dominated the behavior over SSPS, even when an excess of SSPS was added to the dispersions before homogenization.
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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".