Rapid Assessment of Samples from Large-Scale Clandestine Synthetic Drug Laboratories by Soft Ionization by Chemical Reaction in Transfer–High-Resolution Mass Spectrometry

The worldwide ongoing trend of synthetic drug production is also of increasing concern due to enormous amounts of chemical waste produced in clandestine laboratories. Typically, several tons of different types of production waste are stored in numerous containers and need to be characterized after dismantling a laboratory to assess production features, e.g., synthesis route and production scale, and to draw conclusions on the minimum number of batches produced. This forensic assessment is commonly done by a rather laborious gas chromatography - mass spectrometry approach. The aim of this work is to evaluate the suitability of the SICRIT (soft ionization by chemical reaction in transfer) ion source, which is based on the dielectric barrier discharge ionization principle, combined with high-resolution mass spectrometry (HRMS), for the rapid classification of liquid samples from amphetamine production in a seized large-scale clandestine drug laboratory. Among the different sample introduction methods tested, headspace analysis directly into the SICRIT ion source in conjunction with a heated inlet proved to be optimal. Identification of expected target substances (reaction educts, intermediates, byproducts, products) was possible as well as grouping related samples and assigning them to specific synthesis steps by multivariate data analysis in an unsupervised approach. In addition, supervised machine learning algorithms were evaluated to obtain a classification model for the assessment of production waste samples from one dismantled synthetic drug laboratory, and a random forest classifier showed the best performance with an accuracy of 97%. The potential of the novel SICRIT-HRMS approach for the assessment of synthetic drug laboratories was demonstrated.

Authors

Institutions

Publication Details

Journal
Journal of the American Society for Mass Spectrometry
Published
2025-04-30
DOI
https://doi.org/10.1021/jasms.5c00006
Citations
2
Primary Topic
Mass Spectrometry Techniques and Applications
Type
article
Field-Weighted Citation Impact
1.51

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Rapid Assessment of Samples from Large-Scale Clandestine Synthetic Drug Laboratories by Soft Ionization by Chemical Reaction in Transfer–High-Resolution Mass Spectrometry

Michael Pütz, Stephan Wagner, Thomas P. Knepper, Tobias Frömel et al.
2 citations
Journal of the American Society for Mass Spectrometry
Mass Spectrometry Techniques and Applications
1.51
article

Rapid Assessment of Samples from Large-Scale Clandestine Synthetic Drug Laboratories by Soft Ionization by Chemical Reaction in Transfer–High-Resolution Mass Spectrometry

Michael Pütz, Stephan Wagner, Thomas P. Knepper, Tobias Frömel, Carolin Hühn, Maximilian Greif
article en
2 citations

Abstract

The worldwide ongoing trend of synthetic drug production is also of increasing concern due to enormous amounts of chemical waste produced in clandestine laboratories. Typically, several tons of different types of production waste are stored in numerous containers and need to be characterized after dismantling a laboratory to assess production features, e.g., synthesis route and production scale, and to draw conclusions on the minimum number of batches produced. This forensic assessment is commonly done by a rather laborious gas chromatography - mass spectrometry approach. The aim of this work is to evaluate the suitability of the SICRIT (soft ionization by chemical reaction in transfer) ion source, which is based on the dielectric barrier discharge ionization principle, combined with high-resolution mass spectrometry (HRMS), for the rapid classification of liquid samples from amphetamine production in a seized large-scale clandestine drug laboratory. Among the different sample introduction methods tested, headspace analysis directly into the SICRIT ion source in conjunction with a heated inlet proved to be optimal. Identification of expected target substances (reaction educts, intermediates, byproducts, products) was possible as well as grouping related samples and assigning them to specific synthesis steps by multivariate data analysis in an unsupervised approach. In addition, supervised machine learning algorithms were evaluated to obtain a classification model for the assessment of production waste samples from one dismantled synthetic drug laboratory, and a random forest classifier showed the best performance with an accuracy of 97%. The potential of the novel SICRIT-HRMS approach for the assessment of synthetic drug laboratories was demonstrated.

Journal of the American Society for Mass SpectrometryVol. 36(6)
Bundeskriminalamt (DE), Hochschule Fresenius (DE), Institute of Forensic Science (CN), University of Tübingen (DE)
Bundesministerium für Bildung und Forschung, Horizon 2020 Framework Programme
Openalex Percentile: Top 19%
Mass Spectrometry Techniques and Applications
1.51
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.