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Record W2486022463 · doi:10.1007/s11746-016-2872-z

Deterioration Kinetics of Crude Palm Oil, Canola Oil and Blend During Repeated Deep‐Fat Frying

2016· article· en· W2486022463 on OpenAlexafffund
Marie‐Josée Dumont, Michael Ngadi

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2016
Typearticle
Languageen
FieldChemistry
TopicEdible Oils Quality and Analysis
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCanolaChemistryPeroxide valueArrhenius equationCorrelation coefficientDeep fryingPalm oilKineticsActivation energyFood scienceOrganic chemistryMathematics

Abstract

fetched live from OpenAlex

Abstract The oxidation of vegetable oils is generally treated as an apparent first order kinetic reaction. This study investigated the deterioration of crude palm oil (CPO), refined canola oil (RCO) and their blend (CPO:RCO 1:1 w/w) during 20 h of successive deep‐fat frying at 170, 180 and 190 °C. Kinetics of changes in oil quality indices, namely, free fatty acid (FFA), peroxide value (PV), anisidine value ( p ‐AV), total polar compounds (TPC) and color index (CI) were monitored. The results showed that FFA and PV accumulation followed the kinetic first order model, while p ‐AV, TPC and CI followed the kinetic zero order model. The concentration and deterioration rate constants k , increased with increasing temperatures. This effect of temperature was modeled by the Arrhenius equation. The results showed that PV had the least activation energies E a (kJ/mol) values of 5.4 ± 1 (RCO), 6.6 ± 0.7 (CPO) and 11.4 ± 1 (blend). The highest E a requirement was exhibited by FFA with a range of 31.7 ± 3–76.5 ± 7 kJ/mol for the three oils. The overall E a values showed that the stability of the blend was superior and not just intermediate of CPO and RCO. The correlation of the other oil quality indices with TPC indicated a positive linear correlation. The p ‐AV displayed the strongest correlation, with mean correlation coefficient r s of 0.998 ± 0.00, 0.994 ± 0.00 and 0.999 ± 0.00 for CPO, RCO and blend, respectively.

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 score0.032
Threshold uncertainty score0.511

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 head0.010
GPT teacher head0.241
Teacher spread0.231 · 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

Citations26
Published2016
Admission routes2
Has abstractyes

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