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Record W2985485302 · doi:10.3390/foods8110586

The Compositional and Functional Attributes of Commercial Flours from Tropical Fruits (Breadfruit and Banana)

2019· article· en· W2985485302 on OpenAlexafffund
Shiqi Huang, Mario M. Martínez, B. M. Bohrer

Bibliographic record

VenueFoods · 2019
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFood scienceWheat flourStarchComposition (language)ChemistrySwelling capacityRetrogradation (starch)SwellingMaterials scienceAmylose

Abstract

fetched live from OpenAlex

The objective of this study was to compare the compositional and functional properties of tropical flour sources (two breadfruit flours (type A and type B) and a banana flour) with a more traditional flour source (wheat flour). Macro-nutrient composition, pH, water and oil holding capacity, bulk density, particle size, solubility, swelling power, pasting properties, and thermodynamics (gelatinization and retrogradation) were determined. All flours evaluated were similar in their composition with high levels of carbohydrates (greater than 82.52 g/100 g on a dry-matter basis), with most of the carbohydrate content comprised of starch (greater than 67.02 g/100 g). The tropical fruit flours had greater (p < 0.05) water holding capacity than wheat flour. Breadfruit flour B had the lowest (p < 0.05) bulk density, while banana flour had the greatest (p < 0.05) bulk density. The swelling power of the tropical flours was greater (p < 0.05) than the wheat flour. The viscosity of the tropical flours was higher than wheat flour but decreased significantly when temperature was held at 130 °C. These results indicated that the two breadfruit flours and banana flour have great potential for application in processed food products, and have similar compositional attributes to a more traditional flour.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.277

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 head0.024
GPT teacher head0.234
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 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

Citations42
Published2019
Admission routes2
Has abstractyes

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