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Development, Characterization, and Utilization of Food-Grade Polymer Oleogels

2016· review· en· W2228347103 on OpenAlexafffund
Maya Davidovich‐Pinhas, Shai Barbut, Alejandro G. Marangoni

Bibliographic record

VenueAnnual Review of Food Science and Technology · 2016
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolymerEthyl celluloseMaterials scienceRheologySolventPolymer scienceChemical engineeringFood productsStructuringCharacterization (materials science)Organic chemistryChemistryFood scienceNanotechnologyComposite materialBusiness

Abstract

fetched live from OpenAlex

The potential of organogels (oleogels) for oil structuring has been identified and investigated extensively using different gelator-oil systems in recent years. This review provides a comprehensive summary of all oil-structuring systems found in the literature, with an emphasis on ethyl-cellulose (EC), the only direct food-grade polymer oleogelator. EC is a semicrystalline material that undergoes a thermoreversible sol-gel transition in the presence of liquid oil. This unique behavior is based on the polymer's ability to associate through physical bonds. These interactions are strongly affected by external fields such as shear and temperature, as well as by solvent chemistry, which in turn strongly affect final gel properties. Recently, EC-based oleogels have been used as a replacement for fats in foods, as heat-resistance agents in chocolate, as oil-binding agents in bakery products, and as the basis for cosmetic pastes. Understanding the characteristics of the EC oleogel is essential for the development of new applications.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.035
GPT teacher head0.287
Teacher spread0.252 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations171
Published2016
Admission routes2
Has abstractyes

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