Essential Oils and Supercritical CO2 Extracts of Arctic Angelica (Angelica archangelica L.), Marsh Labrador Tea (Rhododendron tomentosum) and Common Tansy (Tanacetum vulgare)—Chemical Compositions and Antimicrobial Activities
Bibliographic record
Abstract
Traditionally, arctic Finnish Angelica (Angelica archangelica L.), marsh Labrador tea (Rhododendron tomentosum, syn. Ledum palustre) and common tansy (Tanacetum vulgare) have been used as medicinal herbs in folklore medicine. However, these underutilised plants are a source of, e.g., oil-based compounds, which could benefit many modern applications implemented by the green chemistry extraction methods, as well. We extracted Angelica, marsh Labrador tea and common tansy by non-toxic and recyclable extraction methods, i.e., hydrodistillation and supercritical carbon dioxide (scCO2) extraction; characterised the essential oils (EOs) and scCO2 extracts by combination of gas chromatography and mass spectrometry (GC-MS), and in addition, analysed the antimicrobial properties. As expected for Angelica root and common tansy inflorescence, the scCO2 extraction method produced less amount of volatile compounds compared to hydrodistillation. On the other hand, more coumarins, alkanes, fatty alcohols and fatty acids were obtained. Additionally, sesquiterpenoids palustrol and ledol were predominant compounds in both marsh Labrador tea EO and scCO2 extract. According to our results, however, all the EOs and scCO2 extracts showed broad spectrum of antimicrobial activities against the selected microbes, but the effects were extract-specific. The strongest and broadest antimicrobial activities were performed by marsh Labrador tea scCO2 extract, which showed extremely strong effect on Staphylococcusaureus subsp. aureus and strong effect on Candida albicans.
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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".