Supercritical CO2 Extraction of Oil from Dried Avocado (Persea Americana Mill.) Fruit Pulp: Oil Yield, Solubility, and the Oil Characteristics
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".