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Record W2525041604 · doi:10.1002/jsfa.8048

Relation between ultrasonic properties, rheology and baking quality for bread doughs of widely differing formulation

2016· article· en· W2525041604 on OpenAlexaff
Donatella Peressini, Dobrila Braunstein, J. H. Page, Anatoliy Strybulevych, Corrado Lagazio, Martin G. Scanlon

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2016
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsRheologyUltrasonic sensorMaterials scienceFood scienceUltrasoundAttenuationComposite materialChemistryAcousticsOptics

Abstract

fetched live from OpenAlex

BACKGROUND: The objective was to evaluate whether an ultrasonic reflectance technique has predictive capacity for breadmaking performance of doughs made under a wide range of formulation conditions. Two flours of contrasting dough strength augmented with different levels of ingredients (inulin, oil, emulsifier or salt) were used to produce different bread doughs with a wide range of properties. Breadmaking performance was evaluated by conventional large-strain rheological tests on the dough and by assessment of loaf quality. The ultrasound tests were performed with a broadband reflectance technique in the frequency range of 0.3-6 MHz. RESULTS: Principal component analysis showed that ultrasonic attenuation and phase velocity at frequencies between 0.3 and 3 MHz are good predictors for rheological and bread scoring characteristics. CONCLUSIONS: Ultrasonic parameters had predictive capacity for breadmaking performance for a wide range of dough formulations. Lower frequency attenuation coefficients correlated well with conventional quality indices of both the dough and the bread. © 2016 Society of Chemical Industry.

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)0.001
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 score0.300
Threshold uncertainty score0.140

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.036
GPT teacher head0.262
Teacher spread0.226 · 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

Citations31
Published2016
Admission routes1
Has abstractyes

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