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Record W2607358496 · doi:10.1002/cjce.22833

Kinetic mechanism of conjugated linoleic acid esterification and production of enriched glycerides as functional oil

2017· article· en· W2607358496 on OpenAlexvenueno aff
Zahra Kouchak Yazdi, Iran Alemzadeh

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsnot available
Fundersnot available
KeywordsConjugated linoleic acidSunflower oilChemistryGlycerolGlycerideLipaseSubstrate (aquarium)TransesterificationFatty acidOrganic chemistryChromatographyLinoleic acidEnzymeBiochemistryCatalysisBiology

Abstract

fetched live from OpenAlex

Abstract Experimental determination of the effects of conjugated linoleic acid (CLA) and glycerol on the rate of enzymatic transesterification were studied to propose suitable mechanistic steps and generate a kinetic model. CLA was suggested due to its purported health benefits and application in preparation of functional foods. CLA, glycerol, and sunflower oil blends with varying concentrations were reacted using a 1, 3‐specific immobilized lipase from Rhizomucor mehei . Scrutiny for mass transfer effects showed that esterification reaction was kinetically controlled. The reaction rate was determined, which showed that affinity of enzyme to CLA is lower than to glycerol. The transesterified lipids were analyzed by gas chromatography for composition of fatty acids and were evaluated for the free fatty acids (FFA). The esterification reaction kinetic follows the Ping Pong Bi Bi mechanism with competitive full (dead end) inhibition by acyl acceptors characterized by the V max , K mCOOH, K mG, and K IG values of 0.328 (mol/L/h), 0.342 (mol/L), 0.04526 (mol/L), and 0.329 (mol/L), respectively. Kinetic model study results indicated how FFAs (CLA and other FFAs) and triacylglycerols (sunflower molecules) can participate as the first substrate in the reaction to produce enriched triacylglycerols as the functional oil.

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 score0.001
Threshold uncertainty score0.196

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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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