Antimicrobial and Antioxidant Activities of Three<i>Mentha</i>Species Essential Oils
Bibliographic record
Abstract
The present study describes the antimicrobial activity and free radical scavenging capacity (RSC) of essential oils from Mentha aquatica L., Mentha longifolia L., and Mentha piperita L. The chemical profile of each essential oil was determined by GC-MS and TLC. All essential oils exhibited very strong antibacterial activity, in particularly against Esherichia coli strains. The most powerful was M. piperita essential oil, especially towards multiresistant strain of Shigella sonei and Micrococcus flavus ATTC 10,240. All tested oils showed significant fungistatic and fungicidal activity [expressed as minimal inhibitory concentration (MIC) and minimal fungicidal concentration (MFC) values, respectively], that were considerably higher than those of the commercial fungicide bifonazole. The essential oils of M. piperita and M. longifolia were found to be more active than the essential oil of M. aquatica. Especially low MIC (4 microL/mL) and MFC (4 microL/mL) were found with M. piperita oil against Trichophyton tonsurans and Candida albicans (both 8 microL/mL). The RSC was evaluated by measuring the scavenging activity of the essential oils on the 2,2-diphenyl-1-picrylhydrazyl (DPPH) and OH radicals. All examined essential oils were able to reduce DPPH radicals into the neutral DPPH-H form, and this activity was dose-dependent. However, only the M. piperita oil reduced DPPH to 50 % (IC50 = 2.53 microg/mL). The M. piperita essential oil also exhibited the highest OH radical scavenging activity, reducing OH radical generation in the Fenton reaction by 24 % (pure oil). According to GC-MS and TLC (dot-blot techniques), the most powerful scavenging compounds were monoterpene ketones (menthone and isomenthone) in the essential oils of M. longifolia and M. piperita and 1,8-cineole in the oil of M. aquatica.
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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) | 0.001 | 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".