Soft Independent Modeling of Class Analogies for the Screening of New Psychoactive Substances through UPLC-HRMS/MS.

The proliferation of NPS has become a global issue, due to their easy availability and ability to bypass drug screening tests. These substances are particularly concerning because of their unpredictable toxicological effects and the analytical challenge in identifying them. The present study combines advanced analytical strategies based on UPLC-HRMS with multivariate analysis to identify and classify unknown NPS. Tandem mass spectrometry (MS/MS) spectra of 159 analytical standards were acquired, retention times and MS data were preprocessed and organized in separate matrices to obtain a training set (including 75% of the analytes) and a test set (with the remaining 25%). Principal component analysis (PCA) revealed distinct clusters for different NPS classes, such as benzodiazepines, JWH, and PINACA, while others, like cathinones and fentanyl analogues, showed greater dispersion. Subsequently, soft independent modeling of class analogies (SIMCA) classification models were built. The models were validated, achieving optimal values, and correctly classifying analytes included in the test set, especially when considering the data obtained at lower collision energy. External validation was conducted using three real seized drug samples. This confirmed the models' ability to classify data not included in the training set, as reflected in the positive validation parameters achieved for each model. Although some misclassifications occurred due to the limited availability of standards for certain classes, the SIMCA models proved highly effective in identifying NPS, demonstrating their value as a reliable tool for supporting forensic investigations.