MétaCan
Menu
Back to cohort
Record W2329284157 · doi:10.1021/jf400305z

Charge Compensation, Phase Diagram, and Protein Aggregation in Soy Protein–Gum Arabic Complex Formation

2013· article· en· W2329284157 on OpenAlexaff
Die Dong, Yufei Hua, Yeming Chen, Xiangzhen Kong, Caimeng Zhang, Qi Wang

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsChemistryIonic strengthPhase diagramAnalytical Chemistry (journal)TurbidityTurbidimetryPhase (matter)Ternary operationChromatographyAqueous solutionPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

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.

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)0.000
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 score0.199
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.213
Teacher spread0.190 · 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

Citations38
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural and Food ChemistrySame topicProteins in Food SystemsFrench-language works237,207