The differentiation
of positional isomers is a well established
analytical challenge for forensic laboratories. As more novel psychoactive
substances (NPSs) are introduced to the illicit drug market, robust
yet efficient methods of isomer identification are needed. Although
current literature suggests that Direct Analysis in Real Time–Time-of-Flight
mass spectrometry (DART-ToF) with in-source collision induced dissociation
(is-CID) can be used to differentiate positional isomers, it is currently
unclear whether this capability extends to positional isomers whose
only structural difference is the precise location of a single substitution
on an aromatic ring. The aim of this work was to determine whether
chemometric analysis of DART-ToF data could offer forensic laboratories
an alternative rapid and robust method of differentiating NPS positional
ring isomers. To test the feasibility of this technique, three positional
isomer sets (fluoroamphetamine, fluoromethamphetamine, and methylmethcathinone)
were analyzed. Using a linear rail for consistent sample introduction,
the three isomers of each type were analyzed 96 times over an eight-week
timespan. The classification methods investigated included a univariate
approach, the Welch
t
test at each included ion;
a multivariate approach, linear discriminant analysis; and a machine
learning approach, the Random Forest classifier. For each method,
multiple validation techniques were used including restricting the
classifier to data that was only generated on one day. Of these classification
methods, the Random Forest algorithm was ultimately the most accurate
and robust, consistently achieving out-of-bag error rates below 5%.
At an inconclusive rate of approximately 5%, a success rate of 100%
was obtained for isomer identification when applied to a randomly
selected test set. The model was further tested with data acquired
as a part of a different batch. The highest classification success
rate was 93.9%, and error rates under 5% were consistently achieved.