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Record W1967413635 · doi:10.1016/j.lwt.2014.02.051

Performance of two immobilized lipases for interesterification between canola oil and fully-hydrogenated canola oil under supercritical carbon dioxide

2014· article· en· W1967413635 on OpenAlexafffund
Ehsan Jenab, Feral Temelli, Jonathan M. Curtis, Yuanyuan Zhao

Bibliographic record

VenueLWT · 2014
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCanolaChemistryInteresterified fatSupercritical fluidSupercritical carbon dioxideChromatographyEnzymeCabin pressurizationCatalysisCarbon dioxideChemical engineeringOrganic chemistryFood scienceLipaseMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The performance and stability of Lipozyme RM IM and TL IM for the interesterification between canola oil and fully-hydrogenated canola oil (FHCO) in the presence of SCCO2 were studied using a high pressure batch stirred reactor at 65 °C/17.5 MPa. The influence of exposure time (4, 8, and 12 h) in SCCO2 and pressurization/depressurization cycles of up to 12 times on the enzyme's resultant activity were studied. There was no significant difference (p > 0.05) in the performance of the two enzymes over reaction time, reaching a constant degree of interesterification of about 23% after 2 h. Although FE-SEM images illustrated some morphological changes on the surface of the enzymes after 4 cycles of 7 h each, similar degrees of interesterification were achieved after each cycle. However, the amount of reaction intermediates decreased by 50–60% in the product obtained by using SCCO2-treated enzymes after 12 pressurization/depressurization cycles compared to untreated enzymes, while there were no significant changes in the conformational and morphological structure of the treated enzymes based on FTIR and FE-SEM analysis. Findings enhance our understanding of enzymatic conversions of lipids under high pressure CO2, targeting production of base-stock for zero-trans margarines.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.616

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 head0.014
GPT teacher head0.241
Teacher spread0.227 · 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

Citations25
Published2014
Admission routes2
Has abstractyes

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