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- E-ISSN 2288-8985
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Impurity profiling and chemometric analysis of methamphetamine seizures in Korea
Analytical Science and Technology / Analytical Science and Technology, (P)1225-0163; (E)2288-8985
2020, v.33 no.2, pp.98-107
https://doi.org/10.5806/AST.2020.33.2.98
(Forensic Genetics & Chemistry Division, Supreme Prosecutors' Office)
(Forensic Genetics & Chemistry Division, Supreme Prosecutors' Office)
(Department of Chemistry, Center for Proteome Biophysics and Chemistry Institute for Functional Materials,)
(Forensic Genetics & Chemistry Division, Supreme Prosecutors' Office)
(Forensic Genetics & Chemistry Division, Supreme Prosecutors' Office)
(Department of Chemistry, Center for Proteome Biophysics and Chemistry Institute for Functional Materials,)
(Forensic Genetics & Chemistry Division, Supreme Prosecutors' Office)
Dong,
W.
S.
, Jae,
C.
C.
, Wonho,
L.
, &
Jin,
Y.
K.
(2020). Impurity profiling and chemometric analysis of methamphetamine seizures in Korea. Analytical Science and Technology, 33(2), 98-107, https://doi.org/10.5806/AST.2020.33.2.98
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Abstract
Methamphetamine (MA) is currently the most abused illicit drug in Korea. MA is produced by chemical synthesis, and the final target drug that is produced contains small amounts of the precursor chemicals, intermediates, and by-products. To identify and quantify these trace compounds in MA seizures, a practical and feasible approach for conducting chromatographic fingerprinting with a suite of traditional chemometric methods and recently introduced machine learning approaches was examined. This was achieved using gas chromatography (GC) coupled with a flame ionization detector (FID) and mass spectrometry (MS). Following appropriate examination of all the peaks in 71 samples, 166 impurities were selected as the characteristic components. Unsupervised (principal component analysis (PCA), hierarchical cluster analysis (HCA), and Kmeans clustering) and supervised (partial least squares-discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), support vector machines (SVM), and deep neural network (DNN) with Keras) chemometric techniques were employed for classifying the 71 MA seizures. The results of the PCA, HCA, K-means clustering, PLS-DA, OPLS-DA, SVM, and DNN methods for quality evaluation were in good agreement. However, the tested MA seizures possessed distinct features, such as chirality, cutting agents, and boiling points. The study indicated that the established qualitative and semi-quantitative methods will be practical and useful analytical tools for characterizing trace compounds in illicit MA seizures. Moreover, they will provide a statistical basis for identifying the synthesis route, sources of supply, trafficking routes, and connections between seizures, which will support drug law enforcement agencies in their effort to eliminate organized MA crime
- keywords
- methamphetamine, impurity profiling, chemometric analysis, GC-FID/MS
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