MétaCan
Menu
Back to cohort
Record W4399692547 · doi:10.1016/j.lwt.2024.116302

Effects of solubility of Supercritical-CO2 solvent and mass transfer property on extraction of vitamin E from canola seeds

2024· article· en· W4399692547 on OpenAlexafffund
John Shi, Sophia Jun Xue, Qingrui Sun, Martin G. Scanlon

Bibliographic record

VenueLWT · 2024
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of ManitobaAgriculture and Agri-Food Canada
FundersGuelph Research and Development Centre, Agriculture and Agri-Food CanadaAgriculture and Agri-Food Canada
KeywordsCanolaSolubilityExtraction (chemistry)Supercritical carbon dioxideSolventSupercritical fluidYield (engineering)ChemistryVitaminMass transferChromatographyVitamin EChemical engineeringMaterials scienceOrganic chemistryFood scienceAntioxidantBiochemistryMetallurgy

Abstract

fetched live from OpenAlex

This study is to explore the solubility of the targeted Vitamin E from canola seeds in supercritical-CO2 (SC-CO2)solvent and their intricate relationship through the developed modelling approach. The study investigated the impact of pressure and temperature on the efficiency of Vitamin E extraction through the regulation of Vitamin E solubility and yield in SC-CO2 solvent. These parameters played a crucial role in determining extraction efficiency. Pressures enhanced the solubility and density of Vitamin E, while elevated temperatures facilitated total Vitamin E extraction yield by promoting solvent diffusion and mass transfer through viscosity reduction. The optimal separation process of Vitamin E was achieved at 75ׄ°C, 18MPa. The Vitamin E concentration in extracted oil is 12 times higher than Vitamin E contents in regular canola seed oil. The mathematical modeling revealed fundamental mechanisms governing the extraction process and offers useful information for optimizing extraction protocols in industrial applications.

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 score0.084
Threshold uncertainty score0.268

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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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

Citations9
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueLWTSame topicPhase Equilibria and ThermodynamicsFrench-language works237,207