Chromatographic analysis and physicochemical evaluation of the essential oil of Bauhinia monandra Kurz flowers
Bibliographic record
Abstract
The study of chromatographic techniques, classical and modern, describes the simplicity and, at the same time, the advances that this area has undergone in recent years in quality scientific research and also in learning in undergraduate and postgraduate courses around the world. This paper investigate a characterization by thin layer chromatography (TLC) and gas chromatography coupled with mass spectrometry (GC-MS), as a method developed by graduate students that involve a combination of a classic and modern technique, as well as results about the physicochemical properties of the essential oil of Bauhinia monandra flower. Essential oil was extracted by Clevenger, the TLC was performed in different eluents and developers, and thus the retention factors (Rfs), and the chemical profile by GC-MS were obtained. The essential oil of the flowers showed a yield of 0.06%, positive solubility in ethanol 70%, refractive index of 1.3621, optical rotation of +36.4αD and relative density of 0.941 g mL-1 at 20 °C. In the TLC analysis 18 Rfs were observed after the use of different developers, with the predominant class of oxygenates compounds. In the GC-MS analysis, 7 compounds were observed, being two majorities, characterized as panaxene with 20.51% and the α-guaiene with 33.39%. The essential oil of B. monandra flower showed a predominance of 70.22% of sesquiterpenic compounds. The allied techniques, classic and modern, demonstrated different ways of evaluating the essential oil through its chemical composition, both techniques showed high efficiency and precision, in addition was an appropriate project developed by postgraduate students.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".