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Record W2782908986 · doi:10.1080/10942912.2017.1396476

Composition and shear crystallization of milkfat fractions extracted with supercritical carbon dioxide

2017· article· en· W2782908986 on OpenAlexaff
Dilek Büyükbeşe, Dérick Rousseau, Ahmet Kaya

Bibliographic record

VenueInternational Journal of Food Properties · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsToronto Metropolitan University
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsButterfatChemistrySupercritical carbon dioxideCrystallizationRaffinateExtraction (chemistry)ChromatographyCarbon dioxideSupercritical fluidSupercritical fluid extractionFood scienceOrganic chemistryMilk fat

Abstract

fetched live from OpenAlex

Milkfat was fractionated at 40°C using supercritical carbon dioxide (CO2) at pressures of 10, 15, and 20 MPa. Fractions were collected for 6 h at each pressure and evaluated for extraction yield, fatty acid composition, thermal transition temperatures, and shear crystallization behavior. The mass extraction yields at 10, 15, and 20 MPa were 34%, 26%, and 33%, respectively, with the remaining raffinate representing ~7% of the starting milkfat. As a function of time within each successive pressure, there was a decrease in the proportion of short-chain (C4–C8), medium-chain (C10–C14), and total saturated fatty acids as well as an increase in long-chain (C18–C18:2) and total unsaturated fatty acids extracted. Shear showed little effect on the growth behavior of any fractions, but showed large effects on nucleation onset. This study showed that the composition and crystallization behavior of fractionated milkfat can be greatly tailored based on supercritical CO2 pressure and extraction time.

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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.230
Teacher spread0.204 · 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".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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