Phytochemical Investigation and Antimicrobial Evaluation of Foeniculum vulgare Leaves Extract Ingredient of Ethiopian Local Liquor
Bibliographic record
Abstract
Medicinal plants are of great interest to the researcher in the field of biotechnology, as natural products, including medicinal plants, accounts 25% of prescribed drugs. Plants are sources for fragrances, drink colors and flavors in several countries including Ethiopia. All parts of Foeniculum vulgare were traditionally used as antispasmodic, aromatic, carminative, digestive, galactagogues, stomach and kidney ailment. Foeniculum vulgare leaves extract was investigated for its phytochemicals as well as antimicrobial effects. The petroleum ether, CHCl3, CHCl3/CH3OH (1:1) and CH3OH crude extract were subjected to phytochemicals screening test which revealed that it is rich in any primary and secondary metabolites such as steroids, tannins, flavonoids, cholesterol, terpenoids, saponins, phenols, cardiac glycosides, carbohydrates, and proteins. The essential oil of the plant leaves was investigated by GC?MS and was found to have (64.92%) anethole, as a major constituent followed by (30.88%) estragole and (3.21%) fenchyl acetate. The crude extracts, oil, and the isolated compound were tested against four bacterial species (Gram negative bacteria, Escherichia coli and Shigella flexneri; Gram positive bacteria: Staphylococcus aureus and Streptococcus pyrogenes) and two fungal species (Fusarium oxysporum and Aspergillus niger) using paper disc diffusion method. Tests of antimicrobial activity showed that all crude extracts and isolated pure compound were active against all the tested bacterial and fungal species. However, the hydrodistillation extract was found to have no antibacterial activity towards the tested bacterial species but active against the two fungal species and thus the present study supported the traditional claims of the plant.
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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 | 0.001 | 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".