Composition analysis and antimicrobial activity of essential oil from leaves of Laurus nobilis grown in Oman
Bibliographic record
Abstract
Traditionally, Laurus nobilis is used for the treatment of earaches and skin rashes as well as rheumatism. The most common technique to isolate the oil is hydro-distillation and steam-distillation. The investigation presented in this paper was aimed to unravel the antimicrobial activity and chemical composition of essential oil in the leaves of L. nobilis grown in Oman. The essential oil was extracted from the leave of the selected plant species by hydro distillation using Clevenger type apparatus. The antimicrobial assay was done by disc diffusion methods against three bacterial strains, Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli, and one fungal strain: Candida albicans. The isolated essential oil was analyzed by gas chromatography-mass spectrometry (GC-MS). The percentage yield of the essential oil was 0.064% (w/w). The essential oil was found actively against the applied bacterial and fungal strains. The highest activity was found against S. aureus and the lowest was against E. coli, and the order was S. aureus > P. aeruginosa > E. coli. The essential oil was also active against the fungus C. albicans. The range of inhibition zones was 6–14 mm. The results of GC-MS analysis showed (E)-β-caryophyllene to be the major component about 59.62%. Other major components included α-selinene (14.03%), α-humulene (8.65%), β-selinene (4.99%), and α-pinene (4.98%). In addition, several minor components were found in the essential oil of L. nobilis. Based on the experimental results, it showed that the plant species that grown in Oman were a potential source of therapeutic agents. In conclusion, the essential oil and the plant could be used as natural and potential therapeutic agents to treat earaches, skin rashes, and rheumatism.
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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".