MétaCan
Menu
Back to cohort

Emulsifiers for the Food Industry

2020· other· en· W4232414171 on OpenAlexaff
Clyde E. Stauffer, Priyatharini Ambigaipalan, Fereidoon Shahidi

Bibliographic record

VenueBailey's Industrial Oil and Fat Products · 2020
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsEmulsionPhase (matter)PolymerAmphiphileChemical engineeringAqueous two-phase systemSurface energyAqueous solutionChemistryPolarMixing (physics)Organic chemistryMaterials sciencePhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Emulsifiers are widely used in the food industry. Emulsions are thermodynamically unstable systems consisting of two immiscible phases, such as oil and water, which could be in the form of either oil‐in‐water (o/w) emulsion or water‐in‐oil (w/o) emulsion. Putting mechanical energy into the system (e.g. by mixing) in a way that subdivided one phase will increase the total amount of interfacial area and energy. Thus, the lower the amount of interfacial free energy per unit area, the larger the amount of new interfacial area that can be created for a given amount of energy input. The subdivided phase is called the dispersed phase, and the other phase is the continuous phase. Emulsifiers are more concentrated in the interfacial region than in the bulk solution phase that is amphiphilic in nature, with the lipophilic (or hydrophobic) part of the molecule preferring to be in a lipid (nonpolar) environment and the hydrophilic part preferring to be in an aqueous (polar) environment. Examples of emulsifiers are phospholipids, proteins, polysaccharides, and other surface‐active polymers. This article emphasizes the thermodynamic approach of emulsion and emulsifiers as well as their industrial food applications in detail.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.325
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0020.001
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 head0.089
GPT teacher head0.235
Teacher spread0.146 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueBailey's Industrial Oil and Fat ProductsSame topicProteins in Food SystemsFrench-language works237,207