Distillation Time Modifies Essential Oil Yield, Composition, and Antioxidant Capacity of Fennel (Foeniculum vulgare Mill)
Bibliographic record
Abstract
Fennel (Foeniculum vulgare Mill) is an essential oil crop grown worldwide for production of essential oil, as medicinal or as culinary herb. The essential oil is extracted via steam distillation either from the whole aboveground biomass (herb) or from fennel fruits (seed). The hypothesis of this study was that distillation time (DT) can modify fennel oil yield, composition, and antioxidant capacity of the oil. Therefore, the objective of this study was to evaluate the effect of eight DT (1.25, 2.5, 5, 10, 20, 40, 80, and 160 min) on fennel herb essential oil. Fennel essential oil yield (content) reached a maximum of 0.68% at 160 min DT. The concentration of trans-anethole (32.6-59.4% range in the oil) was low at 1.25 min DT, and increased with an increase of the DT. Alpha-phelandrene (0.9-10.5% range) was the lowest at 1.25 min DT and higher at 10, 80, and 160 min DT. Alpha-pinene (7.1-12.4% range) and beta-pinene (0.95-1.64% range) were higher in the shortest DT and the lowest at 80 min DT. Myrcene (0.93-1.95% range), delta-3-carene (2.1-3.7% range), cis-ocimene (0-0.23% range), and gamma-terpinene (0.22-2.67% range) were the lowest at 1.25 min DT and the highest at 160 min DT. In contrast, the concentrations of paracymene (0.68-5.97% range), fenchone (9.8-22.7% range), camphor (0.21-0.51% range), and cis-anethole (0.14-4.66% range) were highest at shorter DT (1.25-5 min DT) and the lowest at the longer DT (80-160 min DT). Fennel oils from the 20 and 160 min DT had higher antioxidant capacity than the fennel oil obtained at 1.25 min DT. DT can be used to obtain fennel essential oil with differential composition. DT must be reported when reporting essential oil content and composition of fennel essential oil. The results from this study may be used to compare reports in which different DT to extract essential oil from fennel biomass were used.
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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".