Essential Oil Content and Chemical Composition in 14 Selected Species From a Stretch of Restinga in Southern Brazil
Bibliographic record
Abstract
The restinga is an Atlantic Forest ecosystem characterized by tree, shrub, and herb species that are rich sources of essential oils. In this study, we aim to quantify the essential oil content and determine the chemical constituents of fresh leaves of 14 plant species in a restinga stretch in southern Brazil. Essential oils were obtained by hydrodistillation in a Clevenger-type apparatus and analyzed by gas chromatography coupled to mass spectrometry. Campomanesia reitziana, Cortaderia selloana, and Sophora tomentosa had no essential oils. Total essential oil content ranged from 0.01% (Mikania involucrata) to 1.56% (Varronia curassavica). In total, 60 chemical constituents were identified, representing between 46.2% and 96.5% of the chemical composition of the essential oils. Limonene was the common constituent in all species in which the essential oils were present. The major constituents were ar-curcumene (15.1%) and cis-chrysanthenol (14.2%) in Ambrosia elatior; benzyl benzoate (43.5%) and benzyl salicylate (23.7%) in Aniba firmula; caryophyllene oxide (35.7%) and spathulenol (10.6%) in Austroeupatorium inulaefolium; spathulenol (19.8%) and caryophyllene oxide (14.0%) in Baccharis spicata; caryophyllene oxide (16.3%) in Eugenia astringens; curzerene (30.0%), limonene (13.0%), and germacrone (11.9%) in Eugenia uniflora; caryophyllene oxide (17.1%) and ledol (11.3%) in Lantana camara; caryophyllene oxide (27.7%) and limonene (12.7%) in M. involucrata; 1,8-cineole (19.8%) in Psidium cattleianum; limonene (10.2%) in Schinus terebinthifolius, and allo-aromadendrene (15.2%) in V. curassavica. We expect that our results can assist in selecting species of potential interest for herbal, phytotherapeutic, and cosmetic products.
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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 | 0.001 |
| 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".