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Record W2736262988 · doi:10.1094/cchem-05-17-0084-r

Gluten Aggregation Behavior in High‐Shear‐Based GlutoPeak Test: Impact of Flour Water Absorption and Strength

2017· article· en· W2736262988 on OpenAlexaff
Kun Wang, Brigitte Dupuis, Bin Xiao Fu

Bibliographic record

VenueCereal Chemistry · 2017
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
Fundersnot available
KeywordsFarinographGlutenChemistryAbsorption of waterWheat flourFood scienceWheat glutenComposite materialMaterials science

Abstract

fetched live from OpenAlex

Gluten aggregation behaviors of wheat flour were evaluated using a high‐shear‐based method with the GlutoPeak instrument and related to flour functional properties. GlutoPeak peak time (PT) and peak area (PA) were positively associated with gluten strength but negatively affected by farinograph absorption (FAB). GlutoPeak maximum torque ( T max ) was highly positively ( P < 0.001) correlated with FAB regardless of gluten strength. PT and PA increased with the decrease of FAB. This could result in overestimation of gluten strength owing to water absorption. To account for the impact of FAB, a new parameter GlutoPeak strength index (GSI) was introduced for predicting gluten strength. GSI was obtained by multiplying T max and PA. This arithmetic product was found to provide greater correlation ( r = 0.91) with dough strength than those of PA ( r = 0.84) or PT ( r = 0.57) based on the analysis of 56 advanced breeding lines with wide range of FAB. Moreover, significant relationships were found between GSI and flour mixing and baking properties. Using 8 g of flour and with a test time of less than 10 min, the GlutoPeak instrument shows great potential as a rapid tool for gluten strength selection.

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 score0.220
Threshold uncertainty score0.365

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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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

Citations30
Published2017
Admission routes1
Has abstractyes

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