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Record W4377193681 · doi:10.5539/jfr.v12n3p7

Supercritical CO2 Extraction of Oil from Dried Avocado (Persea Americana Mill.) Fruit Pulp: Oil Yield, Solubility, and the Oil Characteristics

2023· article· en· W4377193681 on OpenAlexvenueno aff
Nam Nghiep Tran, Tuan Q. Dang

Bibliographic record

VenueJournal of Food Research · 2023
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPerseaSupercritical fluidChemistrySupercritical fluid extractionExtraction (chemistry)Pulp (tooth)Peroxide valueChromatographyParticle sizeFood scienceBotanyOrganic chemistry

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the applicability of supercritical fluid technology for extraction of avocado oil. The dried avocado fruit pulp (Persea americana Mill.) was extracted by using a semi-pilot supercritical extraction system. Effect of several process parameters, such as CO2 flow rate, particle size, pressure and temperature on total oil yield, free fatty acid, peroxide value, total phenolic and total flavonoid content in oil were assessed. Avocado oil was extracted within the range for flow rate of 10 and 15L/h, particle size of 2.0 and 3.0 mm, temperature of 34, 42 and 50 oC and pressure of 15, 20, 25 and 30 MPa. Kinetic curves clearly exhibited three periods of extraction (constant rate, falling rate and diffusion-controlled). Increasing flow rate, pressure, temperature or reducing particle size brought an increase in the oil yield and extraction rate. Overall, after 150 min of extraction, the oil in dried avocado was almost completely extracted. The oil yield by supercritical CO2 method (58.97%) obtained at 50 oC and 30 MPa was higher than that by Soxhlet method (55.83%). An increase in pressure (at constant temperature) brought an increase in free fatty acid values in oil but decrease in peroxide values and total phenolic content. On the other hand, the effect of temperature on those parameters was opposite. The oil by supercritical CO2 method was of better quality than that by Soxhlet method.

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.061
GPT teacher head0.326
Teacher spread0.265 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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