Essential Oil Composition of Ruta graveolens L. Fruits and Hyssopus officinalis Subsp. aristatus (Godr.) Nyman Biomass as a Function of Hydrodistillation Time
Bibliographic record
Abstract
The aim of this study was to establish the kinetics regression models for yield and composition of Ruta graveolens fruit and Hyssopus officinalis subsp. aristatus aboveground biomass essential oil (EO), collected at different time intervals during the hydrodistillation process. The hypothesis was that collecting the EO fractions during specific time frames may result in EOs with dissimilar composition that may have differential use by the industry. Furthermore, we calculated the kinetics regression models for the composition of EO, isolated by hydrodistillation in a Clevenger-type apparatus and characterized by GC-MS and GC-FID analyses. The EO yield of R. graveolens fruits was 0.39% (relative area % of GC-FID chromatogram), with major constituents in the Control fraction (0–90 min) being 2-nonanone, 2-undecanone, and 2-undecanol, representing 65% of the total oil. The highest concentration of 2-nonanone (60%) was found in the 30–60 min oil fraction, the concentration of 2-undecanone (35%) was highest in the Control (0–90 min) fraction, and the concentration of eucalyptol (19%) was highest in the 5–10 min fraction. The EO yield of H. officinalis subsp. aristatus dried biomass was 1.12%. The major constituents in the Control fraction (0–90 min) of H. officinalis biomass were eucalyptol, α-pinene, sabinene, β-pinene, and cis-3-pinanone, representing 86% of the total. Eucalyptol (58%) was the highest in the 0–5 min fraction. The highest β-pinene (15%) and cis-3-pinanone (20%) contents were found in the 20–40 min fraction. The kinetics regression models that were developed for EO composition of R. graveolens were second-order polynominal, Michaelis–Menten, and Exponential decay, while for EO composition of H. officinalis subsp. aristatus biomass were Exponential decay and Power. The results from this study could benefit the EO industry.
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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) | 0.000 | 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 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".