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Record W2150422026 · doi:10.1002/jsfa.2199

Comparative study of the polypeptide profiles and functional properties of <i>Sinapis alba</i> and <i>Brassica juncea</i> seed meals and protein concentrates

2005· article· en· W2150422026 on OpenAlexaff
Rotimi E. Aluko, Tara McIntosh, F. Katepa‐Mupondwa

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBrassicaMealChemistryFood scienceComposition (language)Polyacrylamide gel electrophoresisGel electrophoresisBotanyBiochemistryBiologyEnzyme

Abstract

fetched live from OpenAlex

Abstract Defatted meals and protein concentrates from six accessions of Sinapis alba and one accession of Brassica juncea mustard seeds were analysed for their polypeptide profile and functional properties. Two types of protein concentrates were prepared using acid‐induced and calcium‐induced protein precipitations. Meals from the S alba seeds had similar polypeptide composition, which was different from that of the B juncea meal. Non‐reducing sodium dodecyl sulfate polyacrylamide gel electrophoresis showed that two of the major polypeptides (50 and 55 kDa) in S alba seeds were susceptible to acid‐induced precipitation but resistant to calcium‐induced precipitation. The B juncea meal proteins were significantly ( p ≤ 0.05) more susceptible to heat coagulation than the S alba meal proteins. Emulsifying activity index was significantly higher ( p ≤ 0.05) in the B juncea meal and protein concentrates when compared with similar products from S alba . It was concluded that the presence of a high‐molecular‐weight (135 kDa) disulfide‐bonded polypeptide could have contributed to the lower emulsifying power of the S alba products when compared with the B juncea proteins that do not have this polypeptide. Copyright © 2005 Society of Chemical Industry

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)0.001
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 score0.125
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.027
GPT teacher head0.208
Teacher spread0.181 · 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
Published2005
Admission routes1
Has abstractyes

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