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RESPONSE SURFACE METHODOLOGY APPLIED TO THE EXTRACTION OF PHENOLIC COMPOUNDS FROM<i>JATROPHA CURCAS</i>LINN. LEAVES USING SUPERCRITICAL CO<sub>2</sub>WITH A METHANOL CO‐SOLVENT

2009· article· en· W2156623102 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Food Process Engineering · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEssential Oils and Antimicrobial Activity
Canadian institutionsUniversity of Waterloo
FundersRoyal Golden Jubilee (RGJ) Ph.D. Programme
KeywordsMethanolChemistryAqueous solutionResponse surface methodologyExtraction (chemistry)Supercritical fluidChromatographyGallic acidTolueneSolventEllagic acidBox–Behnken designJatropha curcasNuclear chemistryOrganic chemistryBotanyPolyphenolAntioxidant

Abstract

fetched live from OpenAlex

ABSTRACT Response surface methodology was used to analyze the results of experiments designed using the Box–Behnken method to extract three phenolic compounds, gallic acid (GA), corilagin (CG) and ellagic acid (EA), from Jatropha curcas Linn. leaves using supercritical CO 2 and methanol as a cosolvent. Experiments were carried out from 10 to 30 MPa, 40 to 80C and 30 to 70% (v/v) aqueous methanol. A 3 × 3 Box–Behnken design was used to design the experiments to determine the effects of pressure, temperature and concentration of methanol (MeOH) as well as their interaction on the extraction yield. Three nonlinear equations and 3‐D plots (one for each product) with 10 terms were developed. Analytical and numerical techniques were used to locate the optimal operating conditions. The highest experimental yields were obtained at 10 MPa, 60C and 30% (v/v) methanol modifier for GA; 20 MPa, 80C and 30% (v/v) methanol modifier for CG; and 30 MPa, 40C and 50% (v/v) methanol modifier for EA. The response surface models predicted that the maximum extraction yields of GA, CG and EA were 1,567.68 mg/kg of GA at 10 MPa, 80C and 30% (v/v) aqueous MeOH; 4,693.60 mg/kg of CG at 30 MPa, 80C and 30% (v/v) aqueous MeOH; and 1,089.02 mg/kg of EA at 10 MPa, 80C and 70% (v/v) aqueous MeOH, respectively. Because the theoretical optimum was on the limit of the range of the experiments, future work should focus on new experiments designed around the predicted optimum. PRACTICAL APPLICATIONS The purpose of this research was to study the extraction of gallic acid (GA), corilagin (CG) and ellagic acid (EA) from Jatrapha curcas Linn. leaves using supercritical carbon dioxide (SCCO 2 ) with a methanol cosolvent. Polar organic cosolvents or modifiers can be used to enhance extraction yield of polar solutes by increasing the CO 2 polarity. Because methanol (MeOH) has a high polarity index, it was used to extract the three phenolic compounds (polar compounds) in the SCCO 2 process. In addition to extracting GA, CG and EA, this research determined the maximum yield and the effect of operating parameters (pressure, temperature and MeOH concentration) using a response surface quadratic model to determine the location of the optimum operating conditions.

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.

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 score0.013
Threshold uncertainty score0.306

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 science0.0000.000
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 head0.040
GPT teacher head0.285
Teacher spread0.245 · 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