Optimization of Solvent Extraction Method for Stilbenoid and Phenanthrene Compounds in Orchidaceae Species
Bibliographic record
Abstract
This study introduces an optimized and selective extraction methodology using dichloromethane/methanol (DCM/MeOH, 95:5, v/v) in combination with accelerated solvent extraction (ASE) for the targeted stilbenoid and phenanthrene derivatives from five orchid species: Cattleya nobilior (root), Cymbidium defoliatum (root and bulb), Dendrobium phalaenopsis (stem), Encyclia linearifolioides (leaf), and Phalaenopsis aphrodite (root). Sequential extraction was performed with hexane, followed by DCM/MeOH (95:5 and 1:1, v/v) under controlled temperatures (70 °C for hexane, 100 °C for DCM/MeOH), using three static cycles per stage. Chemical profiling by high-performance liquid chromatography with a diode-array-detector and tandem mass spectrometry (HPLC-DAD-MS/MS) enabled the identification of twenty specialized metabolites—seven stilbenoids and thirteen phenanthrenes—several reported here for the first time, including crepidatuol B, dendrosinen D, and coeloginanthridin. The analytical method showed excellent separation of structurally related phenolic compounds, demonstrating the efficiency of the extraction protocol and the selectivity of the solvent system. Many of the identification metabolites are known for cytotoxic, antioxidant, anti-inflammatory, and metabolic regulatory properties, while newly detected compounds remain unexplored and present promising candidates for future biological evaluation. The broad distribution of these metabolites across the studied orchids enhances the current understanding of their phytochemical diversity and suggests chemotaxonomic relevance within the Orchidaceae family. Importantly, the extraction strategy requires minimal plant material, offering ecological advantages when working with rare or endangered species. Overall, this environmentally conscious extraction approach provides a robust platform for metabolic discovery and supports future research in natural products chemistry, plant ecology, drug discovery, structure–activity relationships studies and biotechnological applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".