MétaCan
Menu
Back to cohort
Record W2003081526 · doi:10.1021/jf060961i

Effect of the pH of Heating on the Qualitative and Quantitative Compositions of the Sera of Reconstituted Skim Milks and on the Mechanisms of Formation of Soluble Aggregates

2006· article· en· W2003081526 on OpenAlexaff
Laurence Donato, Douglas G. Dalgleish

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsChemistrySkimmed milkCaseinComposition (language)ChromatographyMicelleWhey proteinFood scienceAqueous solutionOrganic chemistry

Abstract

fetched live from OpenAlex

The effect of the pH of heating (6.3-7.3) on the composition of sera in reconstituted skimmed milks was investigated. A combination of SDS-PAGE analysis and size exclusion chromatography (SEC) combined with an original approach to the analysis of the SEC profiles was performed. The composition of the sera varied greatly when the pH of heating was adjusted below and above the natural pH of milk. The formation, composition, and concentration of heat-induced soluble complexes depended on the combination of the effect of adjusting the pH of the milk and the heat treatment. Two types of mechanism for the formation of soluble aggregates appeared to exist, depending on the pH of the milk. The first type results from the formation of WP/kappa-casein aggregates at the surface of the micelle, and these were detached partially into the serum in larger amount as the pH increased up to 6.7, where it reaches a maximum. The second type of complexes, whose amount increased as the pH of heating increased from 6.7 to 7.3, may be formed between caseins (kappa- but also perhaps some alpha(s)-casein) and aggregated WP resulting in complexes that are smaller in size and with a higher kappa-casein/whey protein ratio than the first type.

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 score0.006
Threshold uncertainty score0.133

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 head0.010
GPT teacher head0.242
Teacher spread0.231 · 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

Citations89
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural and Food ChemistrySame topicProtein Hydrolysis and Bioactive PeptidesFrench-language works237,207