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Record W2340851626 · doi:10.1007/s11746-016-2828-3

Crystallization Kinetics of Coconut Oil in the Presence of Sorbitan Esters with Different Fatty Acid Moieties

2016· article· en· W2340851626 on OpenAlexafffund
Sopark Sonwai, Pawitchaya Podchong, Dérick Rousseau

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCrystallizationSorbitanMelting pointKineticsChemistryCoconut oilChemical engineeringChromatographyOrganic chemistryFatty acidFatty acid esterBiochemistry

Abstract

fetched live from OpenAlex

Abstract Sorbitan esters (SEs) are shown to strongly influence the solidification kinetics and fat crystal morphology, but not polymorphic behaviour, of coconut oil (CNO). Solid‐state SEs (sorbitan monopalmitate, monostearate, and tristearate) affect both the high‐ and low‐melting fractions of CNO by decreasing induction time and by increasing crystallization rate as well as the number concentration of crystals. Liquid‐state SEs (sorbitan monoleate and trioleate) and canola oil do not specifically influence the high‐ or low‐melting fraction of CNO but do slow crystallization and lengthen induction time. The influence of sorbitan monolaurate, the only SE with an acyl group similar to that of CNO, is shown to depend on the crystallization temperature. At temperatures below its melting point, it only affects CNO's high‐melting fraction; at temperatures above its melting point, crystallization of both fractions is extensively decelerated. The lack of influence in polymorphic behaviour by any SE confirms no alteration of subcell arrangement, suggesting a lack of complementarity with the crystallizing fat. Overall, these SEs significantly impact the crystallization kinetics of CNO; however, this largely depends on type and concentration.

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 score0.018
Threshold uncertainty score0.207

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.202
Teacher spread0.193 · 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

Citations24
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Oil Chemists SocietySame topicFood Chemistry and Fat AnalysisFrench-language works237,207