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Record W1991259556 · doi:10.1007/s11746-013-2202-7

Destabilization of Yellow Mustard Emulsion Using Organic Solvents

2013· article· en· W1991259556 on OpenAlexafffund
Solmaz Tabtabaei, Veronica Maria Ataya Pulido, Levente L. Diósady

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2013
Typearticle
Languageen
FieldChemistry
TopicEdible Oils Quality and Analysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEmulsionExtraction (chemistry)SolubilityIsopropyl alcoholChemistryAqueous solutionDissolutionAqueous two-phase systemHexaneSolventChromatographyDimethylformamideOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract An aqueous extraction process was developed consisting of aqueous contact with dehulled yellow mustard flour to recover protein followed by dissolution of the released emulsion in dimethylformamide (DMF) or isopropyl alcohol (IPA) to recover the released oil in the form of single‐phase oil–solvent miscellae suitable for industrial applications. Only some 38 ± 3 % of the oil in the yellow mustard emulsion was extracted using DMF even at high weight ratios since DMF is widely miscible with water, preventing separation of the oil from the emulsion. A ternary phase diagram of DMF/oil/water was prepared and confirmed the limited solubility of the oil in DMF in the presence of water. The use of 31:1 IPA:oil weight ratio could effectively recover over 94 % of the oil in the emulsion; however, multiple‐stage treatment of the emulsion was proven to be more efficient with lower volumes of IPA required to achieve high oil extraction yields. The results suggest that the optimal conditions for multiple‐stage process were four stages using 2:1 IPA:oil weight ratio, with 96 ± 1 % oil recovery from the emulsion.

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 score0.006
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.015
GPT teacher head0.256
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

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

Citations17
Published2013
Admission routes2
Has abstractyes

Explore more

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