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Record W2065745615 · doi:10.1094/cchem.2002.79.6.763

Understanding and Modeling the Processing‐Mechanical Property Relationship of Bread Crumb Assessed by Indentation

2002· article· en· W2065745615 on OpenAlexafffundabout
Zhiqiang Liu, Martin G. Scanlon

Bibliographic record

VenueCereal Chemistry · 2002
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAbsorption of waterComposite materialIndentationModulusWater contentPorosityTexture (cosmology)Young's modulusChemistryMaterials scienceGeotechnical engineering

Abstract

fetched live from OpenAlex

ABSTRACT Measuring fundamental mechanical parameters such as Young's modulus and critical stress is a straightforward and valid approach to evaluating the physical texture of breadcrumb. The objectives of this study were to evaluate whether such fundamental mechanical properties could be measured by indentation techniques such as the AACC crumb firmness method, and then to alter breadmaking conditions so as to model the relationship between these indentation mechanical properties as a function of crumb moisture content and crumb density. Bread was baked according to a short dough process using Canadian western red spring (CWRS) wheat flour. Factors considered in the design of experiments were proofing time, water absorption, crosshead speed, and indenter diameter. Young's modulus and critical stress, measured with 12‐ and 20‐mm cylindrical indenters, were well covalidated with those obtained from a standard compression test. With increases in proofing time and water absorption, a more porous and compliant bread texture led to decreasing Young's modulus and critical stress. Our results revealed a good mapping of mechanical properties to crumb moisture content and density that were correlated to breadmaking conditions, thus permitting more precise prediction of the mechanical properties that determine bread texture.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.232

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 head0.176
GPT teacher head0.287
Teacher spread0.111 · 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

Citations21
Published2002
Admission routes3
Has abstractyes

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