Classification and Correlation of St. John's Wort Extracts by Nuclear Magnetic Resonance Spectroscopy, Multivariate Data Analysis and Pharmacological Activity
Bibliographic record
Abstract
The use of proton NMR spectroscopy allows the analysis of complex multi-component mixtures such as plant extracts by simultaneous quantification of all proton-bearing compounds and consequently all relevant substance classes. Since the spectra obtained are too complicated to be analysed visually, the classification of spectra was carried out using multivariate statistical methods. The spectroscopic data of various extracts of St. John's wort (Hypericum perforatum) samples derived from 4 different accessions extracted with 6 distinct solvents were chemometrically evaluated and calibrated using the partial least square (PLS) algorithm. In a first approach, we found a consistent correlation for the spectroscopic pattern of the extracts and the corresponding IC (50) values derived from non-selective binding to opioid receptors. Consequently, the multivariate data analysis was used to predict the pharmacological efficacy of further St. John's wort extracts on the basis of their proton NMR spectra. In a second approach a PLS 2 model was used to predict the biological activity for eight St. John's wort extracts based on two pharmacological data sets: (i) non-selective binding to opioid receptors and (ii) antagonist effect at corticotrophin-releasing factor type 1 (CRF (1)) receptors. The PLS 2 model confirmed the useful application of the presented approach to assess the quality of medicinal herbs and extracts by spectroscopic analysis derived from bioactivity-related quality parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".