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Record W2039756838 · doi:10.1039/c4fo00577e

Emulsification of algal oil with soy lecithin improved DHA bioaccessibility but did not change overall in vitro digestibility

2014· article· en· W2039756838 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFood & Function · 2014
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLipolysisChemistryEmulsionLecithinFood scienceDigestion (alchemy)Lipid digestionChromatographyBiochemistryLipaseEnzymeAdipose tissue

Abstract

fetched live from OpenAlex

Oil emulsification facilitates digestive lipolysis and subsequent lipid bioaccessibility. This study aimed to understand the effects of emulsification on DHA-rich algal oil digestion and bioaccessibility. An oil/water emulsion (50 : 44 : 6 oil-water-soy lecithin) was subjected to an in vitro digestion model with gastric pH 1.6 or 4.0 and particle size distributions, duodenal stage lipolysis and DHA bioaccessibility were determined. The emulsion was destabilized at gastric pH 1.6, with subsequent slow duodenal lipolysis. With gastric pH 4.0, the emulsion structure remained intact, initial lipolysis proceeded rapidly and DHA bioaccessibility was higher than for bulk oil, a mixture of oil, water and soy lecithin, and the gastric pH 1.6 destabilized emulsion (p < 0.05). However, the extent of lipolysis was not affected by emulsification or gastric pH. Therefore, the presence of an intact emulsion at the start of duodenal digestion, while not impacting the extent of lipolysis, did impact the initial lipolysis and DHA 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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.814

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 studies0.0000.000
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 head0.044
GPT teacher head0.285
Teacher spread0.241 · 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