Effect of tea tree (<i>Melaleuca alternifolia</i>) oil as a natural antimicrobial agent in lipophilic formulations
Bibliographic record
Abstract
There has been increased interest surrounding the use of tea tree oil (TTO) as a natural antimicrobial. In this study, the antimicrobial activity of TTO and its components were investigated in vitro and in a predominantly lipid-based personal care formulation. In vitro, TTO showed minimal inhibitory concentrations of 0.2% (for Saccharomyces cerevisiae and Pythium sulcatum), 0.4% (for Escherichia coli, Bacillus subtilis, and Rhizopus stolonifer), and 0.8% (for Botrytis cinerea). TTO at 0.08%-0.8% was often as efficient as parabens. Comparison of the antimicrobial activities of TTO components showed that terpinen-4-ol and γ-terpinene were generally most effective in inhibiting microbial growth. TTO activity in a personal care product was evaluated through air and water exposure, artificial inoculation, and shelf life studies. While TTO did not increase shelf life of unopened products, it decreased microbial load in products exposed to water and air. Results from this study support that antimicrobial activity of TTO can be attributed to varying levels of its components and that low levels of TTO were effective in reducing microbial growth during the use of the product. This study showed that TTO can act as a suitable preservative system within an oil-based formulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".