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This research introduces and evaluates a novel method that offers the potential of providing objective criteria to forensic microscopical hair comparisons. The method combines hair diameter with numeric characterisations of red, green, and blue colour content as determined with the use of digital imaging at defined locations of the hair. Thirty hairs were collected from each of twenty participants, all with naturally coloured brown hair. The hairs were examined with an Olympus BX53® polarising light microscope and digital images were viewed with an Olympus DP72® camera under 400× magnification. Using Olympus cellSens Entry software, hair diameter was measured at 1000, 1500 and 2000 μm from the base of the root. The Olympus cellSens Entry software uses a red, green and blue (RGB) colour model to quantitatively define the colour of each pixel on an image based on its composition of these three principal colour components. This software was used to collect numerical characterisations of hair colour at each distance interval. The diameter and colour values for each hair were compared using discriminant analysis (DA) and principal component analysis. Although a large amount of intrapersonal variation was observed, the degree of interpersonal variation was greater and enabled the statistical model to differentiate between the hair samples from each participant. The DA model achieved sample reclassification with an error rate of 7.33%. A validation study was conducted on a subset of hair samples from which 18 of the 20 were correctly assigned to the participant from whom they originated. These results support the potential of this method to provide an objective addition to current microscopical hair comparison practices.

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.

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