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Record W3197437825 · doi:10.1021/acs.jafc.1c03022

β-Glucan Interaction with Lentil (<i>Lens culinaris</i>) and Yellow Pea (<i>Pisum sativum</i>) Proteins Suppresses Their <i>In Vitro</i> Digestibility

2021· article· en· W3197437825 on OpenAlexafffund
Ruth T. Boachie, Mieke M. B. Commandeur, Raliat O. Abioye, Edoardo Capuano, Teresa Oliviero, Vincenzo Fogliano, Chibuike C. Udenigwe

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa
KeywordsPisumGlucanSativumField peaPea proteinIn vitroPolysaccharideChemistryBiochemistryElectrophoresisFood scienceBiologyBotany

Abstract

fetched live from OpenAlex

In this study, β-glucan interaction with lentil and yellow pea proteins and the effect on in vitro protein digestibility were investigated. Proteins were mixed with β-glucan at mass ratios of 1:0.5, 1:1, and 1:2. The interaction between β-glucan and the proteins was demonstrated by the decrease in transmittance and surface charge and the increase in particle size of the complexes. Bright-field microscopy showed the formation of aggregates between the biopolymers, although increased molecular size was not observed by discontinuous native polyacrylamide gel electrophoresis. Fluorescence microscopy indicated that β-glucan formed aggregates with lentil proteins, while the interaction with yellow pea proteins appeared as distinct phases of protein within the β-glucan network. The in vitro protein digestibility of lentil and pea protein decreased by 27.3 and 34.5%, respectively, in the presence of a β-glucan mass ratio of 1:2. The findings confirm the possibility to modulate protein digestibility by changing the physical characteristics of a food matrix.

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 score0.075
Threshold uncertainty score0.336

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 head0.014
GPT teacher head0.194
Teacher spread0.180 · 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

Citations34
Published2021
Admission routes2
Has abstractyes

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