Effects of Temperature and Extraction Time on Avocado Flesh (Persea americana) Total Phenolic Yields Using Subcritical Water Extraction
Bibliographic record
Abstract
This paper investigates the optimum extraction temperature for enhanced total phenolic yields extracted from avocado fruit flesh (Persea americana) using subcritical water extraction, as well as the impact of fruit ripeness on phenol extraction efficiency. Additionally, extraction yield against extraction time was investigated for time intervals of 10 min over an overall extraction time of 30 min. The subcritical water conditions studied were 18 bar, 87 mL/min, and temperatures of 105 °C, 120 °C, and 140 °C. The total phenolic compounds content was compared for week one avocado flesh and ripe (week four) avocado flesh, with a four-week ripening period between the two samples. The results show that extracting with subcritical water at 105 °C provides the highest phenolic compounds yields of 0.11% and 0.26% by dried mass for week one and ripe fruit (week four), respectively. The experimental results also indicate that the implementation of lower extraction temperatures on week four avocado (i.e., following the selection of week one avocados and allowing them to ripen over a period of one month) enhances the phenolic compounds extraction yields by more than four times relative to the first week’s sample extract, specifically during the first 20 min of extraction.
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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".