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Record W2741487333 · doi:10.1094/cchem-05-17-0096-r

Effect of Damaged Starch and NaCl Level on the Dough Handling Properties of a Canadian Western Red Spring Wheat

2017· article· en· W2741487333 on OpenAlexafffundabout
Andrea K. Stone, Pierre Hucl, Martin G. Scanlon, Michael T. Nickerson

Bibliographic record

VenueCereal Chemistry · 2017
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of ManitobaUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaWestern Grains Research Foundation
KeywordsStarchFood scienceChemistryRheologyChewinessWheat flourFarinographComposite materialMaterials science

Abstract

fetched live from OpenAlex

The effects of damaged starch and NaCl (1 and 2% w/w [flour weight]) on the dough handling properties of a wheat flour ( Triticum asetivum L. ‘Roblin’) were investigated with rheological and textural methods. Damaged starch levels of the base flour and three remilled flours (using reduction rolls with decreasing gap sizes) were 5.42, 6.23, 7.30, and 8.43%. Rheological measurements on the dough showed that the complex modulus increased and the loss tangent (tan δ) decreased with increasing damaged starch levels in the flour, indicating that greater amounts of damaged starch produced stiffer dough. The base flour produced doughs with the highest creep compliance value ( J max ), whereas the flour with the most damaged starch deformed the least. Higher levels of salt produced stiffer dough that deformed less, as evident by the higher complex modulus and lower creep compliance, compared with 1% NaCl. Damaged starch overall decreased dough stickiness (N), work of adhesion (N·s), and cohesiveness (mm). Increasing the salt content decreased the stickiness of the doughs. Increasing the damaged starch greatly increased dough extensibility at 1% NaCl. The greater amounts of damaged starch in the remilled flour mitigated some of the negative effects of reducing the salt content on the dough machinability.

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 score0.032
Threshold uncertainty score0.970

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 head0.057
GPT teacher head0.260
Teacher spread0.203 · 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

Citations12
Published2017
Admission routes3
Has abstractyes

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