LC-MS-MS Analysis of Dietary Supplements for N-ethyl- -ethyl-phenethylamine (ETH), N, N-diethylphenethylamine and Phenethylamine
Bibliographic record
Abstract
There has been a recent rise in the number of cases of athletes being banned from competition because of positive tests for prohibited substances in their biological specimens. Most of these substances are on the World Anti-Doping Agency (WADA) prohibited list, while others are not specifically named on the list. N-Ethyl-α-ethyl-phenethylamine (ETH), a derivative of phenethylamine (PEA), is one of these unlisted substances and shares chemical and biological effects to the amphetamines, which are listed on the WADA prohibited substances list. It is classified as Category 6B stimulant on the list. This study was directed toward the development of an liquid chromatography tandem mass spectrometry (LC-MS-MS) method for the analysis of ETH in performance-enhancing dietary supplement. A standard was prepared and confirmed by spectroscopic analysis, which was then used to develop the analytical procedure. The procedure was validated and found to have an limit of detection of 2.5 ng/mL, limit of quantification of 5 ng/mL and upper limit of linearity of 500 ng/mL, with within-day variability at the 10-ng/mL level range of 3.88-7.89% (n = 6) and 1.39-3.36% (n = 6) for the 100-ng/mL level. The day-to-day variability was 9.8% for the low control and 3.1% for the high control. The method was used to analyze a variety of dietary supplements for ETH as well as PEA and its N, N-diethyl derivative (NDP).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".