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Record W2575480407 · doi:10.1002/cjce.22789

Pressurized liquid extraction of macauba pulp oil

2017· article· en· W2575480407 on OpenAlexvenueno aff
Caroline Portilho Trentini, Sandra Beserra da Silva, Giovana de Menezes Rodrigues, Vitor Augusto dos Santos Garcia, Lúcio Cardozo‐Filho, Camila da Silva

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant biochemistry and biosynthesis
Canadian institutionsnot available
Fundersnot available
KeywordsChromatographyChemistryPulp (tooth)Extraction (chemistry)SolventEthanolSoxhlet extractorOleic acidOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Abstract The aim of this study was to investigate the extraction of oil from macauba pulp using ethanol and isopropanol as pressurized solvents. Experiments were carried out in a semi‐continuous extractor system at various temperatures (40, 60, and 80 °C) maintaining the pressure fixed at 10 000 kPa and the solvent flow at 3 mL/min and also using conventional extraction (in a Soxhlet). For both methods assessed, higher yields were obtained with the use of ethanol as the solvent. In the pressurized liquid extraction (PLE), an increase in temperature from 40 to 60 °C provided higher yields at 72 min of extraction, which was not influenced by the extraction carried out at 80 °C. This temperature effect was also observed in the extraction kinetics data. The maximum yields obtained by PLE were 44.78 % and 37.12 % with ethanol and isopropanol, respectively, which represents ∼77 % of the yield obtained by conventional extraction. Oleic and palmitic acids are the main fatty acids identified in macauba pulp oil, representing ∼88 % of the fatty acids composition, which was not influenced by the extraction method and solvent used. PLE with ethanol provides oils with higher levels of β‐carotene. The flavonoid content was higher with the use of isopropanol; however, it was not influenced by the method used.

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 score0.008
Threshold uncertainty score0.262

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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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

Citations31
Published2017
Admission routes1
Has abstractyes

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