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PRELIMINARY INVESTIGATION OF GALLIC ACID EXTRACTION FROM <i>JATROPHA CURCAS</i> LINN. LEAVES USING SUPERCRITICAL CARBON DIOXIDE WITH METHANOL CO‐SOLVENT

2009· article· en· W1816911652 on OpenAlexaff
PATUMPORN MANPONG, Supaporn Douglas, Peter Douglas, Suwassa Pongamphai, Wittaya Teppaitoon

Bibliographic record

VenueJournal of Food Process Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Waterloo
FundersRoyal Golden Jubilee (RGJ) Ph.D. Programme
KeywordsJatropha curcasSupercritical carbon dioxideMethanolExtraction (chemistry)Supercritical fluidChemistryJatrophaSolventSupercritical fluid extractionGallic acidChromatographyCarbon dioxideParticle sizeNuclear chemistryOrganic chemistryBotanyAntioxidantBiodiesel

Abstract

fetched live from OpenAlex

ABSTRACT The leaves of Jatropha curcas Linn. contain antioxidant, anticancer and anti‐HIV compounds. This research investigated the extraction of the phenolic compound, gallic acid (GA), from Jatropha curcas Linn. leaves using supercritical carbon dioxide (SCCO2) and methanol co‐solvent. The SCCO2 pilot plant extractor, which was 10 times the capacity of a lab scale unit, was designed and built by our research group using a hydraulic pump instead of high pressure pump. The operating pressure was varied from 20 to 40 MPa; the temperature was varied from 40 to 60C; the particle size was varied from 10 to 40 mesh and the modifier concentration was varied from 0 to 70% (v/v) aqueous methanol. An orthogonal L9 (3)4 experimental design was applied to determine the optimal extracting condition and the effect of the parameters. The largest extraction yield from the experiment was 75.39 mg/kg dry weight of leaves at 40 MPa, 50C and 10–16 mesh particles using a 70% (v/v) methanol modifier. However, from the analysis of L9 (3)4, the optimal operating conditions were at 20 MPa, 50C and 10–16 mesh particles using a 70% (v/v) methanol modifier. It was found that temperature, particle size and modifier concentration had a statistically significant influence (at 85% confidence level) on the GA extraction yield. The fact that pressure was not found to be statistically significant is possibly due to the difficulty in controlling pressure in the pilot plant. PRACTICAL APPLICATIONS A pilot plant scale supercritical fluid extraction unit, which was 10 times the size of typical laboratory units, was designed and built in our lab using a low cost hydraulic pump to replace a high pressure pump. The pilot plant was able to reasonably extract gallic acid from the leaves of Jatropha curcas Linn. Preliminary experiments resulted in an optimal set of operating conditions which can be used as a starting point in future work. The system will eventually be instrumented and controlled to access operating data as well as maintain the operation of the extractor at desired set‐points. The advantage of using SCCO2 for the extraction is to achieve pure quality product. Gallic acid, which is one of the phenolic compounds in physic nut leaves, is characterized by various properties such as anti‐inflammatory, antimutagenic, anticancer, antioxidant and antiviral agent.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2009
Admission routes1
Has abstractyes

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