Essential Oil Yield and Composition of Garden Sage as a Function of Different Steam Distillation Times
Bibliographic record
Abstract
Garden sage ( Salvia officinalis L.) is a medicinal, culinary, ornamental, and essential oil plant with a wide range of ecological adaptation. Garden sage essential oil traditionally is extracted by steam distillation from the above-ground biomass and has widespread applications as an aromatic agent in the food and pharmaceutical industries as well as in perfumery and cosmetics. The hypothesis of this study was that the steam distillation time (DT) may significantly affect essential oil yield and composition of garden sage and, therefore, DT could be used as a tool to obtain oil with different composition. Therefore, the objective was to evaluate the effect of various steam DTs (1.25, 2.5, 5, 10, 20, 40, 80, and 160 minutes) on garden sage oil yield and composition. Most of the oil in the garden sage dry herbage was extracted in 10-minute DT; extending DT up to 160 minutes did not significantly increase oil yields. Overall, 39 oil constituents were identified in the garden sage essential oil. Fourteen oil constituents with the highest concentration in the oil were selected for statistical analyses. Monoterpenes represented the major percentage (58.2% to 84.1%) of oil composition followed by sesquiterpenes (4.0% to 16.1%) and diterpenes (0.3% to 7.6%). Overall, the monoterpene hydrocarbons (α-pinene, camphene, β-pinene, myrcene, and limonene) were eluted early in the steam distillation process, which resulted in their high concentration in the oil at 5- to 10-minute DT and relatively low concentrations in the oil obtained at 160-minute DT. In general, the concentration of sesquiterpenes (β-caryophyllene, α-humulene, and verdifloral) increased with increasing duration of the DT and reached their respective maximum concentrations in the oil at 160-minute DT. The relative concentrations of major constituents, camphor and cis-thujone, in the oil obtained at 2.5-minute DT were higher than in the oils obtained at longer DT. Therefore, if oil with high concentrations of camphor and cis-thujone is desirable, garden sage dried biomass ought to be steam distilled for 2.5 to 5 minutes and the oil collected. If oil with a high concentration of monoterpene hydrocarbons and a high concentration of oxygenated monoterpenes is desirable, then garden sage should be distilled for 20 minutes. If oil with a high concentration of the diterpene manool is desirable, then garden sage should be steam-distilled for 80 minutes. If oil with a high concentration of sesquiterpenes is desirable, then garden sage should be steam-distilled for 160 minutes. The duration of steam distillation can be used as an economical method to obtain garden sage oil with a different chemical composition. The regression models developed in this study can be used to predict garden sage oil yield and composition distilled for various amounts of time and to compare literature reports in which different durations of DT were used.
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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".