Essential Oil Composition and Yield of Anise from Different Distillation Times
Bibliographic record
Abstract
Anise ( Pimpinella anisum L.) is a spice, an essential oil crop, and a medicinal plant with a long history of use. Anise seed oil is extracted from anise seed through steam distillation. There is no experimentally established optimal time for distillation of anise seed. We hypothesized that the distillation time (DT) can be customized for optimum yield and composition of anise essential oil. In this study, we determined the effect of nine steam DTs (5, 15, 30, 60, 120, 180, 240, 360, and 480 minutes) on essential oil yield and essential oil composition of anise seed. We developed regression models to predict essential oil yield, the concentration of individual constituents, and the yield of these constituents as a function of DT. Highest essential oil yield (2.0 g/100 g seed, 2%) was obtained at 360-minute DT. The concentration of transanethole, the major anise oil constituent, varied from 93.5% to 96.2% (as a percent of the total oil) and generally was high at 15- to 60-minute DT and low at 240- to 480-minute DT. However, the yield of transanethole (calculated from the essential oil yield and the concentration of transanethole in the oil) increased with increasing DT to reach maximum at 360-minute DT. The concentration of the other oil constituents varied significantly depending on the DT, and some of them were higher at the shorter DT than at the longer DT. However, the yields of these constituents were highest at longer DT (either 360 or 480 minutes). DT can be used to obtain anise essential oil with different composition that would benefit the essential oil industry. This study demonstrated the need for providing DT in reports where anise seed essential oil yield and composition are discussed. This article can also be used as a reference point for comparing studies in which different DTs were used to extract essential oil from anise seed.
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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 | 0.000 |
| 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".