Identification of NBOMe and NBOH in blotter papers using a handheld NIR spectrometer and chemometric methods

A handheld near-infrared (NIR) spectrometer combined with statistical models can rapidly and non-destructively identify whether blotter papers contain N-benzylphenethylamine drugs (NBOMe or NBOH series) or not. The method uses two models: Model A distinguishes samples with any drug from those without, and Model B differentiates NBOMe from NBOH drugs. Partial least squares discriminant analysis (PLS-DA) achieved efficiency rates above 97% in training and validation. Model B remained robust with mean efficiency above 89% after random sample permutation. SIMCA models matched Model A's performance but were less effective for Model B, though better at handling drugs not in the training set. This approach offers a fast screening tool for forensic analysis before chromatography and mass spectrometry.

Study at a glance

Abstract

Abstract N-benzylphenethylamines derivatives, such as NBOMe and NBOH series, are potent hallucinogen drugs that are usually sold in the illicit market as blotter papers containing “legal” LSD alternatives. The identification of these drugs is mainly performed by liquid or gas chromatography coupled with mass spectrometry, but there is a lack of a rapid screening methods to identify samples containing or not drugs of these series. For this purpose, this work offers a fast and non-destructive method applying a handheld NIR spectrometer for discrimination of drugs absorbed in blotter papers using PLS-DA and SIMCA. The method was developed in a two-stage approach: Model A for identification of samples containing or not drugs and model B for identification of samples containing NBOMe or NBOH drugs. PLS-DA models have provided efficiency rates higher 97% in both training and validation phases. Robustness of model B was evaluated with a random permutation of samples among training and validation phases, given mean efficiency rates higher than 89%. SIMCA models presented equivalent efficiency for model A, but lower efficiency for model B comparing to PLS-DA results. However, SIMCA showed a higher efficiency to deal with samples containing drugs not included in the training phase. Results support use of this method for in loco forensic analysis and as a screening method prior the chromatographic and mass spectrometry analysis.