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 compute a mass spectral similarity score between spectra from the sample and reference and make a judgment using both the score and their visual interpretation of the spectra. This approach is inherently subjective and not ideal when 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 non-trivial task. In this manuscript, 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 on-going work.

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

“…Approximately 0.6% of the similarity scores in the datasets were computed between the spectra of the same compound and the rest between different compounds. The raw mass spectra, computed datasets, and source code and scripts used to analyze results are available for review …”

Updates to the Inverted Library Search Algorithm for Mixture Analysis

“…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. 14,15 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.…”

“…In this note, we provide a revised description of the ILSA that includes these advancements. A simple software implementation of the ILSA to supplement this note is available for review, and an interactive implementation of the ILSA is included with the NIST/NIJ DART-MS Data Interpretation Tool …”