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Record W2116615945 · doi:10.1021/bp0499350

Effect of pH on the Ternary Solution Behavior of β-Lactoglobulin

2004· article· en· W2116615945 on OpenAlexaff
Dulce M. Abascal, Robert W. Lencki

Bibliographic record

VenueBiotechnology Progress · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIsoelectric pointChemistryTernary operationEthanolAqueous solutionMorphology (biology)Beta-lactoglobulinPhase (matter)Chemical engineeringChromatographyWhey proteinOrganic chemistry

Abstract

fetched live from OpenAlex

Ternary phase diagrams (TPDs) were constructed for aqueous beta-lactoglobulin solutions containing ethanol and (NH4)2SO4 at pHs of 7, 5, and 3 for temperatures between 20 and 70 degrees C. The addition of (NH4)2SO4 generally led to the production of a reversible precipitate, a transformation that was not strongly influenced by temperature or pH. In contrast, at pH 7 and 20 degrees C, ethanol concentrations >12% led to the formation of a molten-globule structure, which gelled at protein concentrations >10%. Destabilization of beta-lactoglobulin structure occurred at lower ethanol concentrations as temperature was increased, until at 70 degrees C, all solutions that were previously liquid at room temperature had transformed into a gel. At pH 5.0, near beta-lactoglobulin's isoelectric point, demixing dominated, leading to the creation of either irreversible precipitates or a paste-like microgel. Elevated temperatures caused the previously liquid morphology to transform into either a reversible aggregate or microgel. Solution behavior at pH 3 had characteristics of what was observed at pHs 7 and 5. At moderated protein and ethanol concentrations, a paste-like microgel was observed, whereas at higher ethanol concentrations, beta-lactoglobulin formed a gel. This work demonstrates how small changes in protein structure at the molecular level can have a dramatic effect on macroscopic morphology.

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 score0.160
Threshold uncertainty score0.260

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 communication0.0000.000
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 head0.017
GPT teacher head0.249
Teacher spread0.232 · 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

Citations6
Published2004
Admission routes1
Has abstractyes

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