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Record W2625263890 · doi:10.1002/cjce.22922

Pressurized liquid extraction of oil from soybean seeds

2017· article· en· W2625263890 on OpenAlexvenueno aff
Giovana de Menezes Rodrigues, Lúcio Cardozo‐Filho, Camila da Silva

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsnot available
Fundersnot available
KeywordsExtraction (chemistry)Yield (engineering)HexaneEndothermic processSolventChemistryChromatographySoybean oilEthanolLinoleic acidMaterials scienceFatty acidOrganic chemistryBiochemistryAdsorption

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the extraction of soybean oil using ethanol under pressurized conditions. The effects of pressure (10 and 20 MPa), temperature (30 to 90 °C), and solvent flow (1 and 3 mL/min) were evaluated applying different extraction times. In addition, the thermodynamics parameters of the process were determined. It was observed that the pressure exerted no influence on the extraction kinetics, while an increase in temperature promoted better yields. The use of the highest solvent flow increased the initial extraction rate, but did not influence the yield under equilibrium conditions. Based on the thermodynamic parameters, the extraction was classified as endothermic (44.11 kJ/mol), irreversible (143.52 J/mol K), and partially spontaneous (0.60 to −8.01 kJ/mol), and an increase in the temperature favoured this spontaneity. Extraction using the classical Soxhlet method was evaluated for comparison purposes using ethanol and n ‐hexane as solvents and ethanol showed a better extraction efficiency. The maximum yield obtained with pressurized liquid extraction (PLE) was 24.42 %, which represents ∼94 % of the yield obtained by classical extraction. Linoleic and oleic acids were the main fatty acids identified in the soybean oil, representing ∼76 % of the fatty acids composition, which was not influenced by the extraction method or the temperature used in the PLE.

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 score0.009
Threshold uncertainty score0.996

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 science0.0010.000
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 head0.020
GPT teacher head0.206
Teacher spread0.186 · 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

Citations60
Published2017
Admission routes1
Has abstractyes

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