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Record W2755946383 · doi:10.1002/cjce.23020

Sequential extraction methods applied to <i>Piper hispidinervum</i>: An improvement in the processing of natural products

2017· article· en· W2755946383 on OpenAlexvenueno aff
Guilherme Evaldt Rossa, Rafael Nolibos Almeida, Rubem Mário Figueiró Vargas, Eduardo Cassel, Guillermo Moyna

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEssential Oils and Antimicrobial Activity
Canadian institutionsnot available
Fundersnot available
KeywordsPiperaceaeEugenolSteam distillationExtraction (chemistry)Supercritical fluidSupercritical fluid extractionChromatographyEssential oilSafroleChemistrySolventDistillationPiperOrganic chemistryBotanyBiology

Abstract

fetched live from OpenAlex

Abstract The Piperaceae family is represented by herbaceous plants, shrubs, and, more rarely, trees. Among the aromatic varieties of this genus, Piper hispidinervum C. DC. stands out. In this work, volatile and non‐volatile extracts from P. hispidinervum were obtained through the application of sequential extraction methods. The results of steam distillation experiments indicated that optimal conditions were achieved when the extractions were conducted at 101 kPa and an initial bed porosity of 0.91. Chemical analysis showed that safrole is the major component present in the essential oil. NMR analysis revealed the presence of 6‐methoxy eugenol for P. hispidinervum extracts obtained by supercritical fluid extraction using water as co‐solvent at 20 MPa and 313.15 K. Mathematical models adjusted well to the experimental data in all conditions investigated. The methodology proposed represents an improvement in the processing of natural products, since the same material is submitted to different technologies, obtaining different compounds with distinct properties.

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)0.001
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 score0.042
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.264
Teacher spread0.242 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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