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Record W2318439379 · doi:10.1021/jf405131d

Textural and Rheological Properties of Oat Beta-Glucan Gels with Varying Molecular Weight Composition

2014· article· en· W2318439379 on OpenAlexaff
Yolanda Brummer, Cheryl Defelice, Ying Wu, Melissa G. Y. Kwong, Peter Wood, Susan M. Tosh

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2014
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of TorontoAgriculture and Agri-Food Canada
Fundersnot available
KeywordsGlucanRheologyBeta-glucanChemistryMicrostructureSugarComposition (language)ChromatographyFood scienceMaterials scienceBiochemistryCrystallographyComposite material

Abstract

fetched live from OpenAlex

The impact of oat β-glucan concentration and molecular weight (MW) on gel properties was investigated. Mixed MW gels/viscous solutions at 3, 4, and 5% β-glucan with high molecular weight (HMW):low molecular weight (LMW) ratios of 0:100, 25:75, 50:50, 75:25, and 100:0 were evaluated. The 100:0 and 50:50 gels had the lowest tan δ values. The 50:50 gels had the highest storage moduli ( G ′), whereas 100:0 solutions did not gel. Peak melting temperature ( T P ) was highest for 0:100 gels and decreased with the addition of HMW β-glucan. Hardness, at 40% compression, increased with concentration, and 25:75 and 50:50 gels were hardest at each concentration. Ordered microstructure, apparent in 0:100 gels, diminished with HMW β-glucan addition. Glucose addition resulted in lower tan δ values and firmer, harder gels compared to gels without glucose. Thus, the textural properties and melting profiles of β-glucan gels can be manipulated by adjusting the ratios of molecular weight fractions or addition of sugar for different 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 score0.007
Threshold uncertainty score0.278

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 head0.182
Teacher spread0.172 · 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

Citations62
Published2014
Admission routes1
Has abstractyes

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