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Record W4253861054 · doi:10.32920/ryerson.14647038

Fat Crystal Spheroids – Formation, Characterization, Structure Modification, and use as Encapsulation Matrices

2021· preprint· en· W4253861054 on OpenAlexaff
T. T. Tran

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSpheroidDifferential scanning calorimetryCrystallizationRheologyMaterials scienceShear rateChemical engineeringPolarized light microscopyCanolaCrystal (programming language)RheometerMicrostructureSphericityCrystallographyChemistryComposite materialThermodynamics

Abstract

fetched live from OpenAlex

Model fat systems consisting of fully-hydrogenated canola oil (FHCO) and canola oil (CO) were shear-crystallized using a rheometer with a parallel plate geometry at various cooling rates (0.2 to 5.0 ºC/min) and shear rates (0, 500, 1000, and 2000 s-1) to produce spheroidal fat crystals. These spheroids were characterized via rheology, polarized light microscopy (PLM), differential scanning calorimetry (DSC), and x-ray diffraction (XRD). Crystal spheroid formation was optimal at 1.0 ºC/min and viscosity profiles followed a three phase sigmoidal shape. PLM analysis revealed that spheroid size decreased with increased shear rate while sphericity increased. A multi-step mechanism was proposed for the formation of these crystal spheroids. Subsequently, different emulsifiers were used to modify the structure of these crystal spheroids and it was found that the type and concentration of emulsifier had significant effects on spheroid microstructure. Below a critical concentration, emulsifier could be incorporated into the crystal matrix of FHCO while above they would crystallize independently. DSC analysis revealed additional melting fractions compared to the control that were attributed to emulsifier incorporation and co-crystallization with FHCO. XRD showed that the crystallized spheroids were mainly of the βʹ polymorph regardless of the presence or type of emulsifier. A water phase was then introduced within these systems to study their encapsulation potential. Emulsifier type significantly affected crystal shell morphology and encapsulation efficacy. The liquid-state emulsifiers (GMO and PGPR) showed limited interaction with FHCO, with GMO delaying the interfacial crystallization of FHCO while PGPR excluded FHCO from the droplet interface completely. Of the solid-state emulsifiers (GMS, GMP, SMS, and STS), the MAGs produced smooth-surfaced crystal shells around the emulsion droplets while the sorbitan-based emulsifiers produced irregularly-shaped shells and droplet cores (SMS) or incomplete crystal shell formation (STS). The shear-crystallization of our model fat blend also resulted in the formation of cylindrical crystalline assemblies. The average diameter size of these crystal cylinders decreased with increased shear rate. A “log-rolling” mechanism was proposed for their formation. These results demonstrate that laminar shear may be used to modify fat crystal microstructure and induce the formation of spheroidal and cylindrical crystalline assemblies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002

Distilled classifier scores by category (both heads)

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 head0.018
GPT teacher head0.214
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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