Improved LC-MS Detection of Opioids, Amphetamines, and Psychedelics Using TrEnDi
Bibliographic record
Abstract
Substances of misuse are becoming increasingly difficult to analyze as unique methods of smuggling are adopted and due to the rapid emergence of new psychoactive substances, increasing the pool of compounds to characterize and identify. Technologies such as gas chromatography and liquid chromatography coupled to mass spectrometry (MS) represent the gold standard for accurate and robust analysis, with on-site ambient- and portable-MS systems providing rapid methods of drug screening and testing. For many samples containing residual analyte quantities, methods to improve sensitivity through chemical derivatization are critical for accurate determination. Herein, we demonstrate for the first time the use of trimethylation enhancement using diazomethane (TrEnDi) to improve the MS-based sensitivity of 13 different drugs of misuse. All analytes were successfully permethylated, with 11 demonstrating improved analytical characteristics from TrEnDi with MS sensitivity enhancements ranging from 1.2-fold to as high as 24.2-fold in the case of psilocybin, as well as increases in reversed-phase chromatographic retention for most species. Derivatization using 13 C-isotopically labeled TrEnDi reagents were used to successfully resolve isobaric interference issues between three pairs of controlled substances. By using an unconventional aprotic solvent system for electrospray ionization, the benefit of a fixed-permanent positive charge was highlighted as TrEnDi-modified amphetamine was easily measured while unmodified was not detected. Finally, TrEnDi was employed to boost the sensitivity of morphine in a real urine matrix. Our results demonstrate a percent recovery of 103.1% and a sensitivity enhancement of 2.4-fold, demonstrating the versatility and applicability of TrEnDi to pre-existing analytical workflows for trace analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".