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Amino acid composition, foaming, emulsifying properties and surface hydrophobicity of mustard protein isolate as affected by pH and NaCl

2012· article· en· W2133841940 on OpenAlexaff
Mohammed Aïder, Djamel Djenane, Wassef Ben Ounis

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsChemistryEmulsionComposition (language)ChromatographyAspartic acidGlutamic acidAmino acidFood scienceBiochemistry

Abstract

fetched live from OpenAlex

Summary Amino acid composition, protein hydrophobicity, foaming and emulsifying properties of mustard protein isolate at pH 3, 5, 7 and in 0.05 and 0.1 m NaCl were studied. Glutamic (19.18 ± 0.30%) and aspartic (7.49 ± 0.11%) acids were the dominants. Foaming ability was enhanced by NaCl. Time to reach 75 mL foam was 23% higher in water than in NaCl. Drained volume after 10 min was concentration dependent and was the lowest in 0.05 and 0.1 m NaCl at protein concentration of 2.5% and 5%. The emulsifying properties were pH and concentration dependent, and the best results were obtained at pH 3, corresponding to the highest positive charge density of the protein surface. The highest emulsion stability (90.22 ± 3.52%) was obtained in 0.05 m NaCl and 5% protein concentration, whereas the lowest (63.00 ± 1.06%) was in water at all protein concentrations. Protein hydrophobicity was low and depended of pH but not of NaCl.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.021
GPT teacher head0.237
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations32
Published2012
Admission routes1
Has abstractyes

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