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Fractionation of Milk Fat by Short-Path Distillation

2003· article· en· W2162895599 on OpenAlexaff
Rodrigo Campos, Jerrold W. Litwinenko, Alejandro G. Marangoni

Bibliographic record

VenueJournal of Dairy Science · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsChemistryDistillationSupercoolingFractionationChromatographyCrystallizationThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Fractionation of milk fat by short-path distillation changes the chemical composition and physical properties of the resulting fractions. Increases in distillation temperature from 125 to 250 degrees C increased distillate yield from 0.3 to 42.7% (wt/wt). The distillate was enriched in short- and medium-chain fatty acids and low molecular weight acylglycerols, while the retentate was enriched in long-chain saturated and unsaturated fatty acids as well as high molecular weight acylglyerols. As distillation temperature increased, dropping points of the distillate increased. Relative to native milk fat, the solid fat content (SFC) vs. temperature melting profile of the distillate was depressed and that of the retentate was augmented, which correlated with the saturated long-chain fatty acid content in the fractions. Retentate crystallization parameters obtained by fitting the Avrami model to SFC-time data, did not change as a function of distillation temperature, but varied as a function of the degree of undercooling. Changes in microstructure observed by polarized light microscopy also appeared to be solely a function of the degree of undercooling, with no observable differences between retentates obtained at the different distillation temperatures. In addition, no changes in the retentate's free energy of nucleation (deltaGc) as a function of distillation temperature were found. The compressive storage modulus of the crystallized retentate increased as a function of increasing distillation temperature.

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 score0.091
Threshold uncertainty score0.138

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.235
Teacher spread0.219 · 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

Citations48
Published2003
Admission routes1
Has abstractyes

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