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Record W2803213854 · doi:10.1111/ijfs.13823

Influence of enzymatic hydrolysis, pH and storage temperature on the emulsifying properties of canola protein isolate and hydrolysates

2018· article· en· W2803213854 on OpenAlexafffund
Adeola M. Alashi, Christopher Blanchard, Rodney J. Mailer, Samson Agboola, A.J. Mawson, Rotimi E. Aluko

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCharles Sturt UniversityUniverzita Karlova v Praze
KeywordsHydrolysateCanolaChemistryHydrolysisEnzymatic hydrolysisProteolysisTrypsinPepsinEmulsionChromatographyEnzymeFood scienceBiochemistry

Abstract

fetched live from OpenAlex

Summary The aim of this work was to enhance emulsification properties of canola proteins through enzymatic proteolysis and pH variaton. Canola protein isolate ( CPI ) and hydrolysates ( CPH s) were used to form emulsions at pH 4.0, 7.0 and 9.0 followed by storage at 4 or 25 °C for 7 days. Controlled enzymatic hydrolysis led to increased peptide bond cleavage with time (0.23 g/100 g in CPI to 7.18 g/100 g after 24‐h Alcalase hydrolysis). Generally, oil droplet sizes were smaller for emulsions made at pH 9.0, which suggest better quality than those made at pH 4.0 and 7.0. Trypsin hydrolysate emulsions were the most physically stable at pH 7.0 and 9.0; in contrast, the pepsin hydrolysate emulsions were unstable at all conditions. The results suggest that selective enzymatic hydrolysis could play an important role in enhancing successful incorporation of canola proteins and peptides into food systems as protein emulsifiers.

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)0.001
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 score0.005
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.226
Teacher spread0.211 · 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

Citations21
Published2018
Admission routes2
Has abstractyes

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