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Record W3047354301 · doi:10.3390/foods9081071

Modification of Physicochemical Properties of Breadfruit Flour Using Different Twin-Screw Extrusion Conditions and Its Application in Soy Protein Gels

2020· article· en· W3047354301 on OpenAlexafffund
Shiqi Huang, Laura Román, Mario M. Martínez, B. M. Bohrer

Bibliographic record

VenueFoods · 2020
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExtrusionFood scienceSoy proteinMaterials scienceMoistureViscosityStarchSoy flourSwellingChemistryComposite material

Abstract

fetched live from OpenAlex

The objective was to modify functional properties of breadfruit flours using twin-screw extrusion and test the physicochemical properties of the extruded flours. Extruded breadfruit flours were produced with twin-screw extrusion using different last barrel temperature (80 °C or 120 °C) and feed moisture content (17% or 30%). These conditions resulted in four extruded flours with different mechanical (specific mechanical energy, SME) and thermal (melt temperature) energies. At temperatures below the gelatinization of the native starch (<70 °C), swelling power was increased in all extruded treatments. Solubility was dramatically increased in high-SME extruded flours at all tested temperatures. Water holding capacity was dramatically increased in the low-SME extruded flours. A two-fold higher cold peak viscosity was obtained for low SME-high temperature extruded flour compared with the other extruded flours. Low SME-low temperature extruded flour still exhibited a hot peak viscosity, which occurred earlier than in native flour. Setback was decreased in all extruded flours, especially in high-SME treatments. The incorporation of extruded flours into soy protein gels did not affect cooking loss, while hardness and springiness decreased with the addition of extruded flours. Overall, extrusion of breadfruit flour altered functional flour properties, including water holding capacity and pasting properties, and modified the texture of soy protein gels.

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 score0.047
Threshold uncertainty score0.379

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 head0.075
GPT teacher head0.285
Teacher spread0.210 · 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

Citations15
Published2020
Admission routes2
Has abstractyes

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