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Record W2979627926 · doi:10.1039/c9fo02210d

Lipid digestion of oil-in-water emulsions stabilized with low molecular weight surfactants

2019· article· en· W2979627926 on OpenAlexafffund
Natalie Ng, Peter X. Chen, Saeed M. Ghazani, Amanda J. Wright, Alejandro G. Marangoni, H. Douglas Goff, Iris J. Joye, Michael A. Rogers

Bibliographic record

VenueFood & Function · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of WaterlooUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsLipid digestionChemistryDigestion (alchemy)Pulmonary surfactantChemical engineeringWater in oilFood scienceEmulsionChromatographyBiochemistryEnzymeLipase

Abstract

fetched live from OpenAlex

Altering sn-fatty acid position of glycerol mono-oleate (GMO) from sn-1 to sn-2 decreases fatty acid bioaccessibility by 25.9% providing possible strategies to tailor lipemic responses of food emulsions. Lipid digestion kinetics and fatty acid bioaccessibility of monomodal O/W emulsions stabilized at their minimum surfactant concentration (0.5 < MSC > 0.7 (w/w)) were studied in the TNO Intestinal Model (TIM-1) gastrointestinal (GI) tract. No significant differences were observed between induction times nor rate constants when using 1-GMO and 1-GMS, Span 60, Tween 60 and Tween 80 as surfactants in O/W emulsions, as determined by fitting a three-parameter shifted logistic model to the cumulative bioaccessibility. Comparable trends were observed between area under the curve (AUC) of the absolute bioaccessibility and total overall bioaccessibility.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.007
GPT teacher head0.174
Teacher spread0.167 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2019
Admission routes2
Has abstractyes

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