Distillation Time Modifies Essential Oil Yield, Composition, and Antioxidant Capacity of Fennel (Foeniculum vulgare Mill)
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it