Chemical composition and biological activities of fennel ( <i>Foeniculum vulgare</i> Mill.) essential oils and ethanolic extracts of conventional and organic seeds
Bibliographic record
Abstract
Essential oils and ethanolic extracts of seeds of seven organically and conventionally grown accessions (E1, E2, E3, E4, E5, E6, and E7) of fennel (Foeniculum vulgare Mill.) were examined for their chemical constituents, antioxidant and antibacterial activities. Accession E7 presented the highest essential oil yields of 4.30 ± 0.45 and 3.24 ± 0.46%, respectively, for organic and conventional mode. However, E6 noted the lowest ethanolic extract yield in the two modes of cultivation. Gas chromatography/mass spectrometry analysis of the essential oils revealed the presence of nine essential components in all plant materials. (E)-anethole, methyl-chavicol, fenchone, and limonene were highly abundant. A considerable antioxidant effect was shown for all accessions and a significant difference (p < .05) was reported in the mode of cultivation of essential oils and ethanolic extracts. Concerning antibacterial activity, the essential oils showed generally higher antibacterial activity on gram-positive and gram-negative bacteria than ethanolic extracts for organic and conventional fennel seeds. Practical applications Foeniculum vulgare is an aromatic plant widely used nowadays as flavoring as well as food preservative in foods, such as ice cream, meat, and fish dishes. In this context, the chemical composition and biological activities of essential oil and ethanolic extract from fennel seeds cultivated in organic and conventional modes were studied. Fennel seeds extracts presented a richness in polyphenols and flavonoids. In addition, they showed considerable antioxidant activity, while essential oils had a higher antibacterial activity. There is increase demand, nowadays, for such natural and safe products for future applications in the food industries.
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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) | 0.000 | 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".