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Record W2898633543 · doi:10.3390/molecules23112808

Effect of In Vitro Digestion on Water-in-Oil-in-Water Emulsions Containing Anthocyanins from Grape Skin Powder

2018· article· en· W2898633543 on OpenAlexaff
Weili Xu, Yang Yang, Sophia Jun Xue, John Shi, Loong‐Tak Lim, Charles F. Forney, Guihua Xu, Bio Sigui Bruno Bamba

Bibliographic record

VenueMolecules · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsGreoUniversity of GuelphAgriculture and Agri-Food Canada
FundersNational Natural Science Foundation of China
KeywordsFood scienceChemistryIn vitroDigestion (alchemy)Grape seedEmulsionChromatographyBiochemistry

Abstract

fetched live from OpenAlex

The effects of in vitro batch digestion on water-in-oil-in-water (W/O/W) double emulsions encapsulated with anthocyanins (ACNs) from grape skin were investigated. The double emulsions exhibited the monomodal distribution (d = 686 ± 25 nm) showing relatively high encapsulation efficiency (87.74 ± 3.12%). After in vitro mouth digestion, the droplet size (d = 771 ± 26 nm) was significantly increased (p < 0.05). The double W1/O/W2 emulsions became a single W1/O emulsion due to proteolysis, which were coalesced together to form big particles with significant increases (p < 0.01) of average droplet sizes (d > 5 µm) after gastric digestion. During intestinal digestion, W1/O droplets were broken to give empty oil droplets and released ACNs in inner water phase, and the average droplet sizes (d < 260 nm) decreased significantly (p < 0.05). Our results indicated that ACNs were effectively protected by W/O/W double emulsions against in vitro mouth digestion and gastric, and were delivered in the simulated small intestine phase.

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)0.000
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 score0.039
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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

Citations35
Published2018
Admission routes1
Has abstractyes

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