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Record W4292870120 · doi:10.3389/frsfm.2022.937152

Impact of deamidation on the physicochemical properties and air-water interfacial behaviour of gliadin nanoparticles

2022· article· en· W4292870120 on OpenAlexafffund
Maryam Moraveji, Azin Sadat, Iris J. Joye

Bibliographic record

VenueFrontiers in Soft Matter · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsDeamidationGliadinIsoelectric pointChemistryChemical engineeringNanoparticleSolubilityMaterials scienceChromatographyOrganic chemistryGlutenBiochemistry

Abstract

fetched live from OpenAlex

Gliadin due to its low water solubility is excellently suited to make biopolymeric nanoparticles through liquid antisolvent precipitation. These gliadin nanoparticles (GNPs) can be utilized to populate and stabilize interfaces. Gliadin, by nature a protein that carries a low charge, can be altered by deamidation. Deamidation effectively alters the physicochemical properties of gliadin through decreasing the protein’s isoelectric point (IEP). The objective of this study is to explore the effect of different degrees of deamidation on gliadin nanoparticle (interfacial) characteristics. Besides altering the physical stability of GNP suspensions, deamidation did alter the functionality of GNPs as interfacial agents. GNP behaviour at the air-water interface is substantially impacted by pH. Particles made with untreated and deamidated gliadin exhibited a greater tendency to adsorb and form strong viscoelastic films at the air-water interface and better foam structures closer to their IEP. The combination of SEM, confocal Raman microscopy and drop tensiometry provides unique insights in the dominant gliadin structures and interactions at the interfacial film. This study provides crucial insights into the potential of deamidation to tailor gliadin and GNP properties for specific food applications.

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 score0.065
Threshold uncertainty score0.434

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 head0.024
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

Citations0
Published2022
Admission routes2
Has abstractyes

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