Avocado oil extraction processes: method for cold-pressed high-quality edible oil production versus traditional production
Bibliographic record
Abstract
Nowadays the avocado fruit (Persea americana Mill.) is widely regarded as an important fruit for its nutritional values, as it is rich in vital human nutrients. The avocado fruit is mainly sold fresh on the market, which however trades also a relevant quantity of second-grade fruits with a relatively high oil content. Traditionally, this oil is extracted from dried fruits by means of organic solvents, but a mechanical method is also used in general in locations where drying systems and/or solvent extraction units cannot be installed. These traditional processes yield a grade of oil that needs subsequent refining and is mainly used in the cosmetic industry. In the late 1990s, in New Zeland, a processing company with the collaboration of Alfa Laval began producing cold-pressed avocado oil (CPAO) to be sold as edible oil for salads and cooking. Over the last fifteen years, CPAO production has increased in many other countries and has led to an expansion of the market which is set to continue, given the growing interest in highquality and healthy food. Avocado oil like olive oil is extracted from the fruit pulp and in particular shares many principles of the extraction process with extra-vergin olive oil. We conducted a review of traditional and modern extraction methods with particular focus on extraction processes and technology for CPAO production.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".