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Title: Statistical analysis of the chemical attribution signatures of 3-methylfentanyl and its methods of production

Abstract

Chemical attribution of the origin of an illegal drug is a key component of forensic efforts aimed at combating illicit and clandestine manufacture of drugs and pharmaceuticals. The results of these studies yield detailed information on synthesis byproducts, reagents, and precursors that can be used to identify the method of manufacture. Here in the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic 3-methylfentanyl, N-(3-methyl-1-phenethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Eighteen crude samples from six synthesis methods were generated, the analysis of which was used to identify signatures (i.e. chemical compounds) that were important in the discrimination of synthetic route. These methods were carefully selected to minimize the use of scheduled precursors, complicated laboratory equipment, number of steps, and extreme reaction conditions. Using gas and liquid chromatographies combined with time-of-flight mass spectrometry (GC-QTOF and LC-QTOF) over 160 distinct species were monitored. Lastly, analysis of this combined data set was performed using modern machine learning techniques capable of reducing the size of the data set, prioritizing key chemical attribution signatures, and identifying the method of production for blindly synthesized 3-methylfentanyl materials.

Authors:
 [1];  [1];  [2];  [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Forensic Science Center
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Materials Science Division
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1466937
Alternate Identifier(s):
OSTI ID: 1523332
Report Number(s):
LLNL-JRNL-739013
Journal ID: ISSN 0039-9140; 892228
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Accepted Manuscript
Journal Name:
Talanta
Additional Journal Information:
Journal Volume: 186; Journal Issue: C; Journal ID: ISSN 0039-9140
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Previous article in issueNext article in issue Keywords Chemical attribution signature; Machine learning; 3-methylfentanyl; Forensic attribution; Chemical forensics; Opioid

Citation Formats

Mayer, Brian P., Valdez, Carlos A., DeHope, Alan J., Spackman, Paul E., and Williams, Audrey M. Statistical analysis of the chemical attribution signatures of 3-methylfentanyl and its methods of production. United States: N. p., 2018. Web. doi:10.1016/j.talanta.2018.02.026.
Mayer, Brian P., Valdez, Carlos A., DeHope, Alan J., Spackman, Paul E., & Williams, Audrey M. Statistical analysis of the chemical attribution signatures of 3-methylfentanyl and its methods of production. United States. doi:10.1016/j.talanta.2018.02.026.
Mayer, Brian P., Valdez, Carlos A., DeHope, Alan J., Spackman, Paul E., and Williams, Audrey M. Mon . "Statistical analysis of the chemical attribution signatures of 3-methylfentanyl and its methods of production". United States. doi:10.1016/j.talanta.2018.02.026. https://www.osti.gov/servlets/purl/1466937.
@article{osti_1466937,
title = {Statistical analysis of the chemical attribution signatures of 3-methylfentanyl and its methods of production},
author = {Mayer, Brian P. and Valdez, Carlos A. and DeHope, Alan J. and Spackman, Paul E. and Williams, Audrey M.},
abstractNote = {Chemical attribution of the origin of an illegal drug is a key component of forensic efforts aimed at combating illicit and clandestine manufacture of drugs and pharmaceuticals. The results of these studies yield detailed information on synthesis byproducts, reagents, and precursors that can be used to identify the method of manufacture. Here in the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic 3-methylfentanyl, N-(3-methyl-1-phenethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Eighteen crude samples from six synthesis methods were generated, the analysis of which was used to identify signatures (i.e. chemical compounds) that were important in the discrimination of synthetic route. These methods were carefully selected to minimize the use of scheduled precursors, complicated laboratory equipment, number of steps, and extreme reaction conditions. Using gas and liquid chromatographies combined with time-of-flight mass spectrometry (GC-QTOF and LC-QTOF) over 160 distinct species were monitored. Lastly, analysis of this combined data set was performed using modern machine learning techniques capable of reducing the size of the data set, prioritizing key chemical attribution signatures, and identifying the method of production for blindly synthesized 3-methylfentanyl materials.},
doi = {10.1016/j.talanta.2018.02.026},
journal = {Talanta},
number = C,
volume = 186,
place = {United States},
year = {2018},
month = {2}
}

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