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

• Published in: Journal of the American Society for Mass Spectrometry 2023-07, Vol.34 (7), p.1248-1262
• Main Authors: Jackson, Glen P., Mehnert, Samantha A., Davidson, J. Tyler, Lowe, Brandon D., Ruiz, Emily A., King, Jacob R.
• Format: Article
• Language: English
• ISSN: 1044-0305; 1879-1123
• DOI: 10.1021/jasms.3c00089

This study aims to resolve one of the longest-standing problems in mass spectrometry, which is how to accurately identify an organic substance from its mass spectrum when a spectrum of the suspected substance has not been analyzed contemporaneously on the same instrument. Part one of this two-part report describes how Rice–Ramsperger–Kassel–Marcus (RRKM) theory predicts that many branching ratios in replicate electron–ionization mass spectra will provide approximately linear correlations when analysis conditions change within or between instruments. Here, proof-of-concept general linear modeling is based on the 20 most abundant fragments in a database of 128 training spectra of cocaine collected over 6 months in an operational crime laboratory. The statistical validity of the approach is confirmed through both analysis of variance (ANOVA) of the regression models and assessment of the distributions of the residuals of the models. General linear modeling models typically explain more than 90% of the variance in normalized abundances. When the linear models from the training set are applied to 175 additional known positive cocaine spectra from more than 20 different laboratories, the linear models enabled ion abundances to be predicted with an accuracy of