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Record W2981993701 · doi:10.1002/cche.10233

Effect of roasting as a premilling treatment on the functional and bread baking properties of whole yellow pea flour

2019· article· en· W2981993701 on OpenAlexafffund
Gina Young, Lindsay Bourré, Peter Fröhlich, Yulia Borsuk, Ashok Sarkar, Elaine Sopiwnyk, Stuart Jones, Adam Dyck, Linda Malcolmson

Bibliographic record

VenueCereal Chemistry · 2019
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsCanadian International Grains Institute
FundersSaskatchewan Pulse Growers
KeywordsRoastingAromaFood scienceFlavorChemistry

Abstract

fetched live from OpenAlex

Abstract Background and objectives There is a growing global interest in partially replacing wheat flour with pulse flours in foods, including bread. However, undesirable flavors associated with pulse flours, especially yellow pea flour, have limited their use in foods. Pretreating pulses prior to milling offers a possible solution for improving the flavor of pulse flours. The objective of this research was to examine the effect of oven roasting and Revtech roasting (with and without steam) on the compositional, functional, and bread baking properties of whole yellow peas. Findings Regardless of the roasting method used, a roasting temperature of 120°C resulted in flours that retained good functionality and bread baking properties with less detrimental changes in flour color. Bread made with peas roasted at 120°C also had reduced aroma and flavor properties compared to bread made with untreated peas. Conclusions The strong aroma and flavor properties of yellow peas can be reduced by pretreating the peas prior to milling using conventional oven roasting and Revtech roasting. By selecting the appropriate roasting temperature, flour functionality for bread baking can be maintained. Significance and novelty Roasting is a useful premilling treatment for yellow peas. Reducing the off‐flavors associated with pulses while maintaining flour functionality will allow for greater use of pulse flours in formulating 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 score0.013
Threshold uncertainty score0.312

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 head0.018
GPT teacher head0.219
Teacher spread0.202 · 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

Citations32
Published2019
Admission routes2
Has abstractyes

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