In Silico Infrared Spectroscopy as a Benchmark for Identifying Seized Samples Suspected of Being N-Ethylpentylone

New psychoactive substances (NPSs) have concerned authorities worldwide, and monitoring them has become increasingly complex. In addition to the frequent emergence of new chemical structures, the composition of adulterants has changed rapidly. Reliable reference data on NPS are not always available, and identifying them has become an operational problem. In this study, we evaluated the infrared spectral data of 68 seized samples suspected of containing a synthetic cathinone (N-ethylpentylone). We used quantum chemistry tools to simulate infrared spectra as a benchmark and obtained infrared spectra for different cathinones, structurally analogous amphetamines, and possible adulterants. We employed these in silico data to construct different chemometric models and investigated the internal and external validation and classification requirements of the models. We applied the best models to predict the classification of the experimental data, which showed that the seized samples did not have a well-defined profile. Infrared spectra alone did not allow N-ethylpentylone to be distinguished from other substances. This study enabled us to evaluate whether experimental, in silico, and applied statistical techniques help to promote forensic analysis for decision-making. The seized samples required in-depth treatment and evaluation so that they could be correctly analyzed for forensic purposes.