Rose essential oils: Current trends, mapping of extraction techniques, chemical analysis, therapeutic applications, and by‐product valorization
Bibliographic record
Abstract
Abstract This paper provides a comprehensive review of recent advancements in the extraction, chemical analysis, therapeutic applications, and valorization of by‐products from rose essential oils, with a focus on emerging trends and technologies. Traditional extraction methods, such as hydrodistillation and steam distillation, are compared with modern techniques, including supercritical CO 2 extraction (SFE), ultrasound‐assisted extraction (UAE), and microwave‐assisted extraction (MAE). These modern techniques offer improved efficiency, yield, and sustainability, while preserving the chemical integrity of bioactive compounds. Influential extraction parameters, such as temperature, pressure, and extraction time, are analyzed to highlight their impact on yield and chemical composition. Special attention is given to the valorization of rose by‐products, which show potential for pharmaceutical, cosmetic, and food industry applications. Studies demonstrate the promising antioxidant, antimicrobial, and anti‐cancer activities of rose by‐products. The growing demand for sustainable extraction methods and high‐quality natural products highlights the importance of optimizing these technologies. This review also discusses future research directions in the field of rose essential oils, focusing on the integration of advanced extraction methods and the development of value‐added products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".