Capturing Current Practices and Characterizing Measurement Reproducibility for Seized Drug Analysis Using Ambient Ionization Mass Spectrometry: An Interlaboratory Study

Abstract

The use of ambient ionization mass spectrometry (AI-MS) to aid in the preliminary screening of seized drug evidence has steadily increased over the past two decades. Unlike gas chromatography–mass spectrometry (GC-MS), where electron ionization using a single quadrupole analyzer is commonplace, a wide range of ionization sources and mass spectrometers can be used in AI-MS. Differences in instrument configuration can lead to substantial variability in the mass spectral data obtained. An interlaboratory study, consisting of 35 participants from 17 laboratories, was conducted to begin to understand the landscape and the differences in the data that are produced. Laboratories analyzed a series of 21 solutions across multiple days using their own instrumental methods. Mass spectra were extracted and compared to understand operator, within-lab, and between-lab reproducibility for common compounds and mixtures observed in seized drug analysis. In addition, five participants analyzed the 21 solutions using prescribed method parameters to measure reproducibility improvements when using identical instrumental conditions. Mass spectral reproducibility, measured through pairwise cosine similarity, was found to be generally quite high, regardless of sample type, instrument type, method, or operator. Low-fragmentation spectra showed the lowest variability, as they were dominated by intact protonated molecule peaks. Several potential issues that increased variability were identified, including carryover from mass calibrants, poor sample introduction, and mass spectrometer inlets that required cleaning. The use of uniform method parameters was shown to increase the reproducibility of mass spectra across laboratories, most notably at higher in-source collision-induced dissociation energies. This study provides initial insights into the current landscape of AI-MS in seized drug analysis and lays the foundation for future studies that can provide needed data for the development of documentary standards, standard methods, and possibly the establishment of error rates.