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Record W3042558570 · doi:10.1002/ejlt.202000134

Canola Proteins Used as Co‐Emulsifiers with Phospholipids Influence Oil Oxidability, Enzymatic Lipolysis, and Fatty Acid Absorption in Rats

2020· article· en· W3042558570 on OpenAlexafffund
Christine Bourgeois, Leslie Couëdelo, Muriel Subirade, Maud Cansell

Bibliographic record

VenueEuropean Journal of Lipid Science and Technology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversité Laval
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsCanolaChemistryRapeseedFood scienceLipolysisPolyunsaturated fatty acidEmulsionFatty acidLipaseChromatographyBiochemistryEnzymeAdipose tissue

Abstract

fetched live from OpenAlex

Abstract The objective of this study is to formulate and characterize oil‐in‐water emulsions with plant‐derived ingredients only, that is, proteins extracted from canola oil bodies, used as co‐emulsifiers with a canola lecithin, and to assess their suitability for food applications. Using the protein extract increases the chemical stability of rapeseed oil emulsions toward oxidation, based on the delay in conjugated diene formation under accelerated storage conditions, and favors pancreatic lipase activity. Bioaccessibility of rapeseed fatty acids is compared in lymph‐duct‐cannulated rats fed oil or emulsion. Fatty acid absorption by the intestine is increased by 78% when the oil is emulsified with canola proteins as co‐emulsifier: 28.7 mg mL −1 versus 16.1 mg mL −1 for oil ( p < 0.05). In vitro lipolysis results are in overall agreement with fatty acid absorption in vivo. Practical Applications : Results obtained for rapeseed oil emulsified with canola proteins and phospholipids suggest that increased bioaccessibility of n‐3 polyunsaturated fatty acids could be offered in vegan food products.

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)0.002
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 score0.153
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.221
Teacher spread0.203 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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