Extraction of Avocado Seed Waste as a Potential Feedstock for Biodiesel Production
Bibliographic record
Abstract
The rising interest in sustainable energy sources has spotlighted biodiesel as a promising alternative to fossil fuels. Avocado seed waste, rich in vegetable oil, presents a potential feedstock for biodiesel production. However, optimizing the extraction process to maximize oil yield and quality is crucial. This study addresses the knowledge gap concerning the impact of drying time and solvent type on oil extraction efficiency from avocado seeds. Here, we show the effects of varying drying times (2, 3, and 4 hours) and using two solvents (96% ethanol and isopropyl alcohol) on the oil yield and quality using Soxhlet extraction. Results indicate increased drying time correlates with reduced moisture content, with values of 79.94%, 63.17%, and 47.39% for 2, 3, and 4 hours, respectively. Comparatively, isopropyl alcohol exhibited a higher fatty acid content (0.718%) than 96% ethanol. The density of oil extracted with 96% ethanol (1.34 g/ml) after 3 hours of drying surpassed that of isopropyl alcohol. These findings suggest that drying time and solvent type significantly influence the extraction efficiency and quality of oil from avocado seeds, highlighting their potential as a viable biodiesel feedstock.
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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) | 0.001 | 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".