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Record W3091489668 · doi:10.1002/cche.10357

Flour and bread making properties of whole and split yellow peas treated with dry and steam heat used as premilling treatment

2020· article· en· W3091489668 on OpenAlexafffund
Elaine Sopiwnyk, Lindsay Bourré, Gina Young, Yulia Borsuk, Shelley Lagassé, Lindsey Boyd, Ashok Sarkar, Stuart Jones, Adam Dyck, Linda Malcolmson

Bibliographic record

VenueCereal Chemistry · 2020
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsCanadian International Grains Institute
FundersSaskatchewan Pulse Growers
KeywordsAftertasteAromaFood scienceChemistryFlavorBranStarchLimitingRaw materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Background and objectives Pulse flours can impart off‐flavors limiting their use in foods. The objective of this study was to examine the flour and bread making properties of whole and split yellow peas treated with dry and steam heat used as a premilling treatment. Findings Particle size distributions and pasting properties of flours milled from heat‐treated peas were altered, and starch damage was reduced compared to flours milled from untreated peas. Breads made with flour milled from heat‐treated peas had similar firmness to breads made with flour milled from untreated peas with no change in volume and minimal impact on crumb structure. Changes in crumb color (lower L *, higher a *) were consistent with color changes observed in the flours. Heat treating whole peas produced breads with decreased aroma, flavor, and aftertaste, whereas heat treating split peas produced breads with lower intensities of aroma, flavor, bitterness, and aftertaste and increased acceptability. Conclusions Revtech dry and steam heating of whole and split yellow peas as a premilling treatment successfully reduced off‐flavors with minimal changes to flour and bread making properties. Significance and novelty Heat treating pulses prior to milling offers greater opportunities for incorporating pulse flours in foods.

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 score0.029
Threshold uncertainty score0.585

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 head0.028
GPT teacher head0.225
Teacher spread0.197 · 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

Citations17
Published2020
Admission routes2
Has abstractyes

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