Rapid assessment of the illegal presence of 1,3‐dimethylamylamine (DMAA) in sports nutrition and dietary supplements using <sup>1</sup>H NMR spectroscopy
Bibliographic record
Abstract
1,3-Dimethylamylamine (DMAA) is a stimulant that can be found in pre-workout sports nutrition and dietary supplements. This practice is illegal because DMAA is not a safe food ingredient but rather an unapproved medicinal compound due to its pharmacological action. In order to determine the DMAA content in such products, a nuclear magnetic resonance (NMR) spectroscopic method was developed and validated (DMAA was quantified as DMAA-HCl). For quantification, the collective integral from two of the methyl groups of the molecule in the range δ 0.92-0.84 ppm was used. The method was linear over the examined range of 1-21 g/kg (R(2) = 0.9937). The recoveries from spiked concentrations (0.1-6 g/kg) ranged between 85% and 105% (96% on average), with a relative standard deviation (RSD) of 1% for an authentic sample. The detection limit was 0.03 g/kg and the quantification limit was 0.08 g/kg (calculated for 75 mg sample weight). The actual DMAA-HCl content in the sample was quantified using calibration curves (external standardization) or 3,5-dinitrobenzoic acid as single-point internal standard. The developed NMR methodology was applied for the analysis of 16 products, from which 9 samples were found positive (the DMAA-HCl concentration varied between 3.1 g/kg and 415 g/kg). The method can be recommended for routine use in food testing, customs or doping control laboratories.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".