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Record W2078361032 · doi:10.13031/2013.21462

Rheological properties of batter systems containing different combinations of flours and hydrocolloids

2006· article· en· W2078361032 on OpenAlexaff
Jun Xue, Michael Ngadi

Bibliographic record

Venue2006 Portland, Oregon, July 9-12, 2006 · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPolysaccharides Composition and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsXanthan gumRheologyConsistency indexChemistryViscoelasticityFood scienceShear thinningFlow propertiesMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The rheological properties of batters formulated using different combinations of wheat,corn and rice flours with two types of hydrocolloids namely methylcellulose (0.5, 1 and 1.5%) orxanthan gum (0.2%) were studied. Control samples were formulated with combinations of flourswithout the added hydrocolloids. The effects of hydrocolloids on the rheological flow characteristicsof the batter systems were measured using a controlled stress rheometer at a temperature of 15oC.The effects of hydrocolloids on dynamic viscoelastic parameters as functions of temperatures wereevaluated. All the batters showed shear thinning behavior with flow behavior indices in the rangefrom 0.34 to 0.67. Addition of xanthan gum lowered flow index values impacting higher degree ofpseudoplasticity to the batter samples compared to methylcellulose. Consistency index of controlbatter samples varied from 0.46 to 69.2 Pa.sn. Addition of hydrocolloids increased the consistencyindex. Xanthan gum (XG) and methylcellulose (MC) significantly increased consistency index valuesof batters. The gums changed the onset temperature of structure development, and storage (Gmax)and loss moduli (Gmax) of the batter systems. However, no effect was observed on peak temperaturein which the G reached maximum value. Xanthan gum increased both Gmax and Gmax whereas athigher concentrations, methylcellulose increased Gmax but lowered Gmax.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.324

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 head0.191
Teacher spread0.175 · 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 designObservational
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

Citations5
Published2006
Admission routes1
Has abstractyes

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Same venue2006 Portland, Oregon, July 9-12, 2006Same topicPolysaccharides Composition and ApplicationsFrench-language works237,207