Novel psychoactive substances (NPS) have been a challenge in forensic laboratories in the United States. Typical analysis of controlled substances is by gas chromatography-mass spectrometry (GC-MS), in which the GC retention time and mass spectrum are compared to a reference standard to make an identification. With the emergence of NPS compounds, reference standards for new compounds may not be readily available. Multivariate statistical methods have been investigated to classify NPS compounds. This work explored linear discriminant analysis (LDA) and soft independent modelling of class analogies (SIMCA) as methods to classify fentanyl analogs according to structural subclass. Four fentanyl subclasses were investigated and were categorized by the location of the substituent on the core fentanyl structure. Three factors were investigated to improve the robustness of the LDA and SIMCA models: variation within a chromatographic peak, instrument variation, and the application of neutral loss data. Overall, the LDA models performed with a 100% successful classification rate for mass spectral data and a 100% successful classification rate for neutral loss data. The SIMCA models performed with a 91% successful classification rate for mass spectral data and an 87% successful classification rate for neutral loss data. Both models were compared to highlight benefits and limitations to each classification method. This work supports the application of multivariate statistical models in forensic laboratories to obtain structural information when reference materials are not available.
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