Factores que influyen en el rendimiento de aceite esencial de <i>Pimenta dioica</i> L. durante hidrodestilación
Bibliographic record
Abstract
Essential oils (EO) derived from different plants, such as Pimenta dioica L., are obtained by hydrodistillation, are highly valued internationally, and require the study of the factors that determine their performance and quality. The objective of this study was to evaluate the effect of heating intensity and water/solids (W/S) ratio on the extraction rate and yield of EO obtained by hydrodistillation of fresh leaves of P. dioica L. Fresh leaves were cut into squares of approximately 1 cm2 and, together with 500 ml of water, subjected to a hydrodistillation process for 6 hours. A 3X2 factorial design with three replications was considered to define the treatments. The factors studied were A/S at three levels (2, 6, and 10) and heating intensity at two levels (105° and 120 °C). Condensate samples were collected and subjected to liquid-liquid extraction with petroleum ether, combining the ether phases, removing water residues, and evaporating the solvent. The AE obtained for each sample was estimated gravimetrically. The results indicated that the water/solids (W/S) ratio, the heating intensity, as well as their interaction, have a significant effect (p < 0.05) on the extraction rate and the final accumulated yield. The conditions of 120 °C and W/S ratios of 2 and 6 were those that generated the highest extraction rate (K = 69.68 ± 11.58 min and 55.48 ± 3.59 min, respectively), as well as the highest yield (737.98 ± 61.5 mg 100 g-1 and 719.99 ± 45.06 mg 100 g-1).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".