The Min-Max Test: An Objective Method for Discriminating Mass Spectra

Abstract

Deciding whether the mass spectra of seized drug evidence and a reference standard are measurements of two different compounds is a central challenge in forensic chemistry. Normally, an analyst will collect mass spectra from the sample and a reference standard under identical conditions, compute a mass spectral similarity score, and make a judgment about the sample using both the similarity score and their visual interpretation of the spectra. This approach is inherently subjective and not ideal when a rapid assessment of several samples is necessary. Making decisions using only the score and a threshold value greatly improves analysis throughput and removes analyst-to-analyst subjectivity, but selecting an appropriate threshold is itself a nontrivial task. In this paper, we describe and evaluate the min-max testa simple and objective method for classifying mass spectra that leverages replicate measurements from each sample to remove analyst subjectivity. We demonstrate that the min-max test has an intuitive interpretation for decision-making, and its performance exceeds thresholding with similarity scores even when the best performing threshold for a fixed dataset is prescribed. Determining whether the underlying framework of the min-max test can incorporate retention indices for objectively deciding whether spectra are measurements of the same compound is an ongoing work.

Updates to the Inverted Library Search Algorithm for Mixture Analysis

“…In particular, we have yet to determine appropriate thresholds for either of the current scoring metrics (FPIE and RevMF). In pure compound analysis using EI-MS, we know that good spectral matches generally have similarity scores greater than 0.75 (i.e., 750) but can also use retention times and replicate mass spectra to further support any conclusions. , With is-CID spectra of mixtures, there is less intuition about what makes a “good” score as there are significantly more variables to consider, such as the number of compounds in the mixture (which is unknown prior to the analysis). We are currently conducting a large-scale evaluation of DART-MS and the ILSA with a collection of casework samples that contained between 3 and 10 identifiable compounds (as determined with GC–MS), and a preliminary takeaway is that the scores (FPIE or RevMF) are much more discriminatory for prominent targets in the mixture spectrum.…”

Expert Algorithm for Substance Identification Using Mass Spectrometry: Application to the Identification of Cocaine on Different Instruments Using Binary Classification Models

“…50,51 Therefore, in terms of drug identifications in a forensic setting, NIST match factors have questionable value in helping practitioners meet admissibility criteria described in Federal Rules of Evidence 702, especially for structurally and spectrally related fentanyl analogs. 52 That said, NIST continues to develop mass spectral comparison tools that help analysts find spectral and structural similarity between questioned spectra and known spectra, 14,38,39,50,52,53 and these tools will unquestionably assist analysts in identifying rare or novel substances in the future.…”

“…Stein’s similarity search algorithm is dependent on the relative abundance of peaks in both the unknown and library spectra, ,, and although probabilities exist for the ranking accuracy of hundreds of searches, probabilistic measures of accuracy are not available on a compound-specific basis. , Therefore, in terms of drug identifications in a forensic setting, NIST match factors have questionable value in helping practitioners meet admissibility criteria described in Federal Rules of Evidence 702, especially for structurally and spectrally related fentanyl analogs . That said, NIST continues to develop mass spectral comparison tools that help analysts find spectral and structural similarity between questioned spectra and known spectra, ,,,,, and these tools will unquestionably assist analysts in identifying rare or novel substances in the future.…”

Expert Algorithm for Substance Identification Using Mass Spectrometry: Statistical Foundations in Unimolecular Reaction Rate Theory

“…These differences within and between instruments have deleteriously affected the ability of algorithms to rank or identify substances correctly. ,,,,,− All of these factors negatively affect the ability of current algorithms to make true positive identifications, even when a reference spectrum of the questioned substance is present in the database. For example, most algorithms typically only provide around 80% accuracy in ranking the correct identity in the #1 position. ,− For reasons that will become apparent later, the inclusion of replicate spectra of each substance can significantly improve identification rates. ,, …”

“…13,37−44 For reasons that will become apparent later, the inclusion of replicate spectra of each substance can significantly improve identification rates. 12,45,46 Various methods have been developed to improve the confidence in substance identification using spectral comparison techniques, such as changing the weighting factors, 40,41,43,44,47 changing the peak selection or abundance-normalization method, 37,48 modifying the results based on experimental information after the spectrum has already been collected, 49 and increasing the size of the library. 12 Other improvements include the use of partial and semipartial correlations 50 and wavelet and Fourier transformations to increase the accuracy of the identification algorithms.…”