Extraction method dominates phytochemical profiles of aromatic medicinal herbs: a comparative HPTLC study
Bibliographic record
Abstract
Abstract The extraction method is a critical determinant of the phytochemical profile obtained from medicinal plants, yet comparative evaluations across multiple extraction techniques and plant species remain limited. In this study, the qualitative influence of commonly used extraction methods on the phytochemical composition of aromatic medicinal herbs was investigated using high-performance thin-layer chromatography (HPTLC). Ten aromatic medicinal herbs belonging to the families Lamiaceae , Apiaceae , and Asteraceae were subjected to six extraction approaches: simple distillation, essential oil (essence) extraction, hydroalcoholic tincture, infusion, decoction, and decoction of distillate. All extracts were analyzed under standardized HPTLC conditions using silica gel 60 F254 plates, a toluene-ethyl acetate (93:7 v/v) mobile phase, anisaldehyde-sulfuric acid derivatization, and a standard as a reference compound. Across all investigated species, the extraction method exerted a stronger influence on band number, intensity, and polarity distribution than botanical differences among the herbs. Essential oil and tincture extracts consistently produced the highest diversity of bands, particularly in higher Rf regions corresponding to non-polar and semi-polar compounds. Aqueous methods, including infusion and decoction, predominantly yielded low-Rf polar bands with reduced overall diversity, while simple distillation showed minimal extraction efficiency. These findings demonstrate that extraction strategy is the primary factor governing qualitative phytochemical outcomes in HPTLC-based herbal analysis. The results highlight the importance of method selection in phytochemical screening and provide a comparative framework for choosing appropriate extraction techniques in pharmacological, cosmetic, and analytical applications.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".