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Record W4200189723 · doi:10.1002/cche.10506

Physicochemical, anti‐nutritional, and functional properties of air‐classified protein concentrates from commercially grown Canadian yellow pea (<i>Pisum sativum</i>) varieties with variable protein levels

2021· article· en· W4200189723 on OpenAlexaffabout
Dora Fenn, Ning Wang, Lisa Maximiuk

Bibliographic record

VenueCereal Chemistry · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsPrairie Improvement NetworkCanadian International Grains Institute
Fundersnot available
KeywordsPea proteinChemistryRaffinoseFood scienceStachyoseStarchField peaEmulsionProtein isolateSucrosePisumBiochemistry

Abstract

fetched live from OpenAlex

Abstract Background and objectives Air classification is a physical process based on particle size and density used to separate pulse flours into protein and starch concentrates. Little information is available on how variety and crude protein content affect physicochemical and functional properties of the air‐classified yellow pea proteins. The objective of this work was to investigate the effect of flour protein content on particle size (PS), yield, protein separation efficiency (PSE), composition, anti‐nutritional factors, and functionality of the air‐classified protein concentrates derived from commercially grown yellow pea varieties. Findings Pea protein concentrates obtained from high‐protein flours had lower PS, PSE, starch, insoluble and total dietary fiber (IDF and TDF), sucrose, raffinose, and stachyose, but higher protein, foaming capacity (FC), and foaming stability (FS) at 30–120 min than that from low‐protein flours. Crude protein of the pea flours had no significant effect on yield, ash, soluble dietary fiber (SDF), phytic acid (PA), trypsin inhibitor activity (TIA), water‐holding capacity (WHC), oil absorption capacity (OAC), and oil emulsion capacity (OEC) of the corresponding protein concentrates. Among pea varieties, PS, yield, starch, IDF, TIA, sucrose, oligosaccharides, and FS at 30 min of the protein concentrates varied significantly but their PSE, protein, ash, SDF, PA, WHC, OAC, OEC FC, and FS at 10, 60–120 min remained similar. Conclusions Air classification is better suited to using high‐protein pea flour due to low oligosaccharides, high‐protein concentration, and foaming properties of resulting protein fractions compared to concentrates derived from low‐protein peas. However, lower PSE could be a concern due to presence of more small starch particles in the high‐protein pea samples. Significance and novelty This study showed the relationships between flour protein content and characteristics of the air‐classified protein concentrates from yellow peas and identified areas for improvement.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.964
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.032
GPT teacher head0.183
Teacher spread0.151 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2021
Admission routes2
Has abstractyes

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