Distillation Time Effect on Lavender Essential Oil Yield and Composition
Bibliographic record
Abstract
Lavender (Lavandula angustifolia Mill.) is one of the most widely grown essential oil crops in the world. Commercial extraction of lavender oil is done using steam distillation. The objective of this study was to evaluate the effect of the length of the distillation time (DT) on lavender essential oil yield and composition when extracted from dried flowers. Therefore, the following distillation times (DT) were tested in this experiment: 1.5 min, 3 min, 3.75 min, 7.5 min, 15 min, 30 min, 60 min, 90 min, 120 min, 150 min, 180 min, and 240 min. The essential oil yield (range 0.5-6.8%) reached a maximum at 60 min DT. The concentrations of cineole (range 6.4-35%) and fenchol (range 1.7-2.9%) were highest at the 1.5 min DT and decreased with increasing length of the DT. The concentration of camphor (range 6.6-9.2%) reached a maximum at 7.5-15 min DT, while the concentration of linalool acetate (range 15-38%) reached a maximum at 30 min DT. Results suggest that lavender essential oil yield may not increase after 60 min DT. The change in essential oil yield, and the concentrations of cineole, fenchol and linalool acetate as DT changes were modeled very well by the asymptotic nonlinear regression model. DT may be used to modify the chemical profile of lavender oil and to obtain oils with differential chemical profiles from the same lavender flowers. DT must be taken into consideration when citing or comparing reports on lavender essential oil yield and composition.
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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 | 0.001 |
| 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 | 0.001 |
| 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".