Distillation Time Effect on Essential Oil Yield, Composition, and Antioxidant Capacity of Sweet Sagewort (Artemisia annua L.) Oil
Bibliographic record
Abstract
Sweet sagewort, also known as sweet wormwood ( Artemisia annua L.), contains essential oil and other natural products. The objective of this study was to evaluate the effect of eight different distillation times (DTs; 1.25 minutes, 2.5 minutes, 5 minutes, 10 minutes, 20 minutes, 40 minutes, 80 minutes, and 160 minutes) on A. annua essential oil and its antioxidant capacity. Highest essential oil yield was achieved at 160-minute DT. The concentration of camphor (8.7% to 50% in the oil) was highest at the shorter DT and reached a minimum at 160-minute DT. The concentration of borneol showed a similar trend as the concentration of camphor. The concentrations of some constituents in the oil were highest at 2.5-minute DT (alpha-pinene and camphene), at 10 minutes (paracymene), at 20 minutes (beta-chamigrene and gamma-himachalene), at 80 minutes [transmuurola-4(15),5-diene and spathulenol], at 80- to 160-minute DT (caryophylene oxide and cis-cadin-4-en-ol), or at 160-minute DT (beta-caryophyllene, transbeta-farnesene, and germacrene-D). The yield of individual constituents reached maximum at 20- to 160-minute DT (camphor) at 80- to 160-minute DT [paracymene, borneol, transmuurola-4(15),5-diene, and spathulenol], or at 160-minute DT (for the rest of the oil constituents). DT can be used to attain A. annua essential oil with differential and possibly targeted specific chemical profile. The highest antioxidant capacity of the oil was obtained at 20-minute DT and the lowest from the oil in the 5-minute DT. This study suggests that literature reports on essential oil content and composition of A. annua could be compared only if the essential oil was extracted at similar DTs. Therefore, DT must be reported when reporting data on essential oil content and composition of A. annua .
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".