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Record W2161054957 · doi:10.1111/1750-3841.12702

Effect of β‐Glucan–Rich Barley Flour Fraction on Rheology and Quality of Frozen Yeasted Dough

2014· article· en· W2161054957 on OpenAlexaff
A. A. Hamed, Sanaa Ragaee, El‐Sayed M. Abdel‐Aal

Bibliographic record

VenueJournal of Food Science · 2014
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsAgriculture and Agri-Food CanadaMinistry of Agriculture, Food and Rural AffairsUniversity of Guelph
FundersArcher Daniels Midland
KeywordsRheologyFood scienceGlutenChemistryGluten freeBarley flourWheat flourCongelationMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Research has shown that prolonged frozen storage of bread dough reduces the quality of the end product. In this study, the effect of air-classified barley flour fraction rich in β-glucan (approximately 25%) on rheology and quality of frozen yeasted bread dough was investigated. Wheat flour (W) was replaced by air-classified barley flour fraction (B) at 10% without or with 1.4% vital gluten to produce β-glucan enriched barley dough (WB) or barley dough plus gluten (WB + G). Dough products were stored at -18 ºC for 8 wk and their rheological properties were investigated weekly. During frozen storage dough extensibility increased, while elastic and viscous moduli decreased. Differential scanning calorimeter and nuclear magnetic resonance data indicated that WB and WB + G dough products contained approximately 10% less freezable water and 9% more bound water compared to the control dough (W). β-Glucan enriched dough also exhibited less changes in gluten network as shown by SEM photographs. The addition of air-classified barley flour fraction at 10% in frozen dough reduced deterioration effects caused by frozen storage via minimizing water redistribution and maintaining rheological properties of frozen dough.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.066
Threshold uncertainty score0.203

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0000.000

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 head0.028
GPT teacher head0.315
Teacher spread0.288 · 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
Published2014
Admission routes1
Has abstractyes

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