Optimizing Jasmine Flower Extraction: A Review of Modern Approaches
Bibliographic record
Abstract
Jasmine, renowned for its enchanting fragrance and therapeutic properties, holds significant cultural, economic, and industrial value globally. Primarily cultivated for its flowers, jasmine is widely used in perfumery, cosmetics, aromatherapy, and pharmaceuticals. Value addition, such as producing essential oils, teas, and skincare products, enhances profitability, extends shelf life, and reduces post-harvest losses. Traditional extraction methods like steam distillation (SD) and solvent extraction (SE) face limitations, including low yield, long extraction times, and degradation of heat-sensitive compounds. To overcome these challenges, innovative technologies such as supercritical fluid extraction (SFE), microwave-assisted extraction (MAE), ultrasound-assisted extraction (UAE), subcritical water extraction (SWE), pulsed electric field (PEF), and cold plasma extraction have emerged. These methods offer higher efficiency, improved yield, and reduced environmental impact. For example, SFE using supercritical CO₂ achieves superior oil yields, while MAE and UAE reduce extraction time and energy consumption. SWE eliminates organic solvents, making it a sustainable alternative, and PEF and cold plasma enhance extraction by disrupting cell membranes. Despite their advantages, challenges such as high equipment costs, scalability, and optimization of parameters remain. Future research should focus on techno-economic analysis, environmental impact assessment, and scalable industrial prototypes. By integrating these advanced technologies, the jasmine industry can achieve sustainable growth, support rural livelihoods, and meet the rising demand for natural and organic products. This review highlights advancements in jasmine processing, emphasizing the potential of innovative extraction methods to revolutionize the industry while preserving its aromatic and therapeutic qualities.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".