MétaCan
Menu
Back to cohort
Record W2592833516 · doi:10.1111/ijfs.13386

Structure, composition and functional properties of storage proteins extracted from bambara groundnut (<i>Vigna subterranea</i>) landraces

2017· article· en· W2592833516 on OpenAlexafffund
Abimbola Kemisola Arise, Ifeanyi D. Nwachukwu, Rotimi E. Aluko, Eric O. Amonsou

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaAfrican Union CommissionNational Research Foundation
KeywordsVicilinStorage proteinVignaLegumeChemistryComposition (language)Protein isolateFood scienceProtein secondary structureBotanyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Bambara groundnut is a protein‐rich traditional legume. In this study, storage proteins were isolated from three bambara landraces. Bambara protein revealed four major protein bands: one broad band at 55 kD a, two medium bands at 62 kD a and 80 kD a and a high molecular weight ( HMW ) protein at 141 kD a. The vicilin (7S) subunits with molecular weight of 55 kD a and 62 kD a were major fractions in bambara storage proteins. Bambara proteins showed two endothermic peaks ranging from 64 to 69 °C and 76 to 90 °C, respectively. Bambara protein isolates had well‐defined tertiary and secondary structures, respectively, at pH 3.0, and this well‐defined structure decreased slightly at higher pH values. The isolates revealed a strong secondary structure dominated by α‐helical conformation. Foaming capacities of bambara proteins were dependent on pH with maximum percentage FC observed at pH 3.0, while the emulsion activity increased with increasing pH for all the isolates. Vicilin (7S) fraction seems to be the major storage protein fraction of bambara. Bambara proteins could serve as excellent ingredients for the formulation of food foams and emulsions.

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 score0.142
Threshold uncertainty score0.421

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.037
GPT teacher head0.239
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

Citations47
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Food Science & TechnologySame topicProteins in Food SystemsFrench-language works237,207