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Capturing Current Practices and Characterizing Measurement Reproducibility for Seized Drug Analysis Using Ambient

Edward Sisco1, Dennis D Leber1, Arun S Moorthy2

  • 1National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States.

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PubMed
Summary
This summary is machine-generated.

Ambient ionization mass spectrometry (AI-MS) shows high reproducibility in seized drug analysis, despite varied methods. Standardized parameters can further improve mass spectral data consistency across laboratories.

Keywords:
ASAP-MSAmbient Ionization Mass SpectrometryDART-MSInterlaboratory StudySeized DrugsSpectral Reproducibility

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Area of Science:

  • Forensic Science
  • Analytical Chemistry
  • Mass Spectrometry

Background:

  • Ambient ionization mass spectrometry (AI-MS) is increasingly used for preliminary screening of seized drug evidence.
  • AI-MS offers flexibility in ionization sources and mass spectrometers, unlike traditional GC-MS.
  • Variability in instrument configuration can significantly impact AI-MS spectral data.

Purpose of the Study:

  • To assess the reproducibility of AI-MS data in seized drug analysis across different laboratories and methods.
  • To identify factors contributing to data variability in AI-MS.
  • To evaluate the impact of standardized methods on AI-MS data reproducibility.

Main Methods:

  • An interlaboratory study involving 35 participants from 17 laboratories.
  • Analysis of 21 solutions using participants' routine AI-MS methods.
  • Comparison of mass spectra using cosine similarity to assess reproducibility.
  • A subset of participants analyzed samples using prescribed method parameters.

Main Results:

  • Generally high mass spectral reproducibility was observed across various sample types, instruments, methods, and operators.
  • Low-fragmentation spectra exhibited the lowest variability, primarily due to intact protonated molecule peaks.
  • Identified sources of variability included mass calibrant carryover, poor sample introduction, and dirty mass spectrometer inlets.
  • Uniform method parameters significantly enhanced interlaboratory reproducibility, particularly with higher in-source collision-induced dissociation.

Conclusions:

  • AI-MS is a robust technique for seized drug screening with inherent high reproducibility.
  • Standardized methodologies can further optimize AI-MS performance and data consistency.
  • Findings support the development of documentary standards and error rates for AI-MS in forensic applications.