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Record W2026265840 · doi:10.1021/jf063211h

Comparison on the Effect of High-Methoxyl Pectin or Soybean-Soluble Polysaccharide on the Stability of Sodium Caseinate-Stabilized Oil/Water Emulsions

2007· article· en· W2026265840 on OpenAlexaff
Jinru Liu, Edita Verespej, Marcela Alexander, Milena Corredig

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFlocculationPectinChemistryPolysaccharideOil dropletEmulsionDynamic light scatteringElectrophoresisSodiumChromatographySpectroscopyAdsorptionChemical engineeringFood scienceOrganic chemistryNanoparticle

Abstract

fetched live from OpenAlex

The interactions of high-methoxyl pectin (HMP) and soybean-soluble polysaccharide (SSPS) with sodium caseinate-stabilized emulsions were investigated using a multitechnique approach, including dynamic light scattering (DLS), electrophoretic mobility measurements, transmission diffusing wave spectroscopy (DWS), and ultrasonic spectroscopy (US). At pH 6.8, both polysaccharides are negatively charged and did not adsorb onto caseinate-coated droplets due to electrostatic repulsion; however, SSPS showed a different behavior compared to HMP in the turbidity parameter 1/l* and sound attenuation parameters measured by DWS and US, respectively. The present study brought the first evidence of the stabilization effect of SSPS in acidified sodium caseinate-emulsions. While destabilization occurred at low polysaccharide concentrations, probably via bridging flocculation, acid-induced aggregation of the oil droplet was completely prevented by 0.2% SSPS or HMP. However, the interaction behavior of SSPS during acidification was different from that of HMP. This was demonstrated by the different development of the parameter 1/l*, droplet sizes, sound attenuation, and velocity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.256
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations50
Published2007
Admission routes1
Has abstractyes

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