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Record W4402416334 · doi:10.3390/foods13182862

Impact of Particle Size on the Physicochemical, Functional, and In Vitro Digestibility Properties of Fava Bean Flour and Bread

2024· article· en· W4402416334 on OpenAlexafffund
Sunday J. Olakanmi, Digvir S. Jayas, Jitendra Paliwal, Rotimi E. Aluko

Bibliographic record

VenueFoods · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsUniversity of LethbridgeUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsParticle sizeFood scienceStarchChemistryEmulsionParticle (ecology)Wheat flourBiologyBiochemistry

Abstract

fetched live from OpenAlex

Fava beans, renowned for their nutritional value and sustainable cultivation, are pivotal in various food applications. This study examined the implications of varying the particle size on the functional, physicochemical, and in vitro digestibility properties of fava bean flour. Fava bean was milled into 0.14, 0.50, and 1.0 mm particle sizes using a Ferkar multipurpose knife mill. Physicochemical analyses showed that the 0.14 mm flour had more starch damage, but higher protein and fat contents. Functionality assessments revealed that the finer particle sizes had better foaming properties, swelling power, and gelation behavior than the coarse particle size. Emulsion capacity showed that for all the pH conditions, 1.00 mm particle size flour had a significantly higher (p < 0.05) oil droplet size, while the 0.5 and 0.14 mm flours had smaller and similar oil droplet sizes. Moreover, in vitro digestibility assays resulted in improved starch digestion (p ˂ 0.05) with the increase in flour particle size. Varying the particle size of fava bean flour had less impact on the in vitro digestibility of the bread produced from wheat–fava bean composite flour, with an average of 84%. The findings underscore the critical role of particle size in tailoring fava bean flour for specific culinary purposes and nutritional considerations.

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 score0.470
Threshold uncertainty score0.109

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 head0.050
GPT teacher head0.265
Teacher spread0.215 · 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

Citations23
Published2024
Admission routes2
Has abstractyes

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