Identification and Quantitation of Ursolic and Oleanolic Acids in Ilex aquifolium L. Leaf Extracts Using 13C and 1H-NMR Spectroscopy
Bibliographic record
Abstract
Leaves of Ilex aquifolium L. have been used for their therapeutic properties. In previous studies, components contained in the leaves were first isolated by various chromatographic techniques. Then, quantitation of oleanolic and ursolic acids, which are responsible for the biological and therapeutic activities of the plant, was performed by HPLC, HPTLC, and somewhat by GC-MS. Our objective was to develop a simple method that allows the identification of compounds contained in the leaves of Corsican I. aquifolium and to quantify ursolic and oleanolic acids. Leaves were successively extracted with hexane and dichloromethane. The extracts were chromatographed on silica gel and the fractions of column chromatography submitted to 13C-NMR analysis, following a computerized method developed in the laboratory. 13C-NMR allowed the identification of various triterpenes including ursolic acid and oleanolic acid. Quantitation of both acids was achieved, for the first time, by 1H-NMR after validation of the method (accuracy, precision, linearity, limit of detection and limit of quantitation). Ursolic and oleanolic acids accounted for 55.3% and 20.8% of the dichloromethane extract, respectively. This represents 1.3% and 0.5% of the mass of dried leaves. 1H-NMR spectroscopy appeared as a powerful tool for a rapid quantitation of biologically active compounds from I. aquifolium.
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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".