Quantitative 1H and 13C NMR and Chemometric Assessment of 13C NMR Data: Application to Anabolic Steroid Formulations
Bibliographic record
Abstract
This study investigates the potential of 1H and 13C NMR for the characterization and classification of anabolic androgenic steroids (AASs) in various formulations. First, twenty AAS formulations, including tablets, capsules, and injectable solutions, were analyzed using 1H NMR for the qualitative identification and quantification of active compounds. The results revealed discrepancies between the labeled and detected substances in several samples, highlighting issues related to product mislabeling and potential health risks. Then, twelve oil-based injectable formulations were examined using 13C NMR, demonstrating its effectiveness in differentiating and quantifying closely related steroid structures that cannot be discriminated with 1H NMR. A chemometric approach from 13C NMR data, based on a principal component analysis (PCA) and hierarchical cluster analysis (HCA), enabled the classification of samples and the identification of key active ingredients.
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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 | 0.001 |
| 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 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".