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Record W2332244681 · doi:10.1021/jf203157a

Molecular Structure, Physicochemical Characterization, and in Vitro Degradation of Barley Protein Films

2011· article· en· W2332244681 on OpenAlexaff
Yichen Xia, Yixiang Wang, Lingyun Chen

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2011
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPlasticizerDifferential scanning calorimetryFourier transform infrared spectroscopyChemical engineeringUltimate tensile strengthGlycerolChemistryDegradation (telecommunications)Materials scienceSolubilityOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Barley protein films were prepared by thermopressing using glycerol as a plasticizer. The combined effects of heating temperature and amount of plasticizer interacted to determine protein conformation and, subsequently, the properties of the film matrix. The film barrier and mechanical properties were systematically investigated using Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), SDS-PAGE, and protein solubility tests. These experiments demonstrated that heat treatment induced barley protein unfolding and then protein aggregation and the formation of covalent disulfide bonds to enhance film strength. Increasing the amount of plasticizer reduced protein denaturation and limited protein interactions, resulting in significantly improved film flexibility at the cost of reduced film moisture barrier property and tensile strength. In vitro degradation experiments demonstrated that barley films were resistant in gastric conditions, yet can still be completely degraded by intestinal enzymes, and they possess low cytotoxicity to Caco-2 cells. The prepared barley films have potential for development as delivery systems for gastric-sensitive bioactive compounds to the intestine for release.

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 score0.020
Threshold uncertainty score0.272

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 head0.007
GPT teacher head0.182
Teacher spread0.175 · 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

Citations25
Published2011
Admission routes1
Has abstractyes

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