Pattern recognition analysis of the class identity recognition efficiency of a portable laser infrared sensor detecting amphetamines and their main precursors

Abstract

We are presenting a pattern recognition analysis assessing the class identity recognition efficiency of a portable laser infrared sensor detecting controlled phenethylamines, i.e. the stimulant and hallucinogenic amphetamines, as well as ephedrines, which are their main precursors. The training set consists of laser infrared spectra of the later compounds and of negatives, which are randomly selected non-amphetamines. The spectra have been recorded in the spectral domain 1405 -1150 cm -1 , preprocessed with a w TE 2 Fisher discriminating function, and then subjected to Principal Component Analysis (PCA). The PCA scores have been used in order to build several pattern recognition systems designed to recognize the class identity of the targeted compounds, i.e. Cluster Analysis and Naive Bayesian Classifier. The detection efficiency obtained for these two systems is presented and discussed in detail.

2C-x and DOx hallucinogens: a systematic review

“…In 2019, Ciochina et al have reported a chemometric application developed to automatize the processing of the GC-IR spectral data obtained with a portable infrared sensor designed to detect illicit phenethylamines, highlighting the remarkable results obtained with PCA [54]. Other pattern recognition systems, using HCA and Naive Bayesian Classifier (NBC), have also been built to automatize and improve the efficiency in recognizing the class identity of amphetamines [55].…”

Physico-chemical analysis, systematic benchmarking, and toxicological aspects of the JWH aminoalkylindole class-derived synthetic JWH cannabinoids

“…• Only 3 of the 16 synthetic cannabinoids JWH analyzed comparatively still have a value between 1-3, so moderate solubility, moderate permeability, and low metabolism, JWH 133 (2,448), JWH 161 (2,190), and JWH 302 (2,033).…”

“…In this regard, physico-chemical analysis, biological and toxicological evaluation of synthetic compounds, precursors and derivatives, highlighting patterns of consumption of psychoactive substances, spectral methods of characterization and identification, artificial intelligence, and expert systems are among the most effective methods for the identification, research, and testing of new drugs or emerging chemical compounds, being, at the same time, formidable tools in the fight against trafficking networks of high-risk substance [2].…”

Convolutional Neural Network Detecting Synthetic Cannabinoids

“…Despite the increasing number of psychoactive substances found on the black market, only few comprehensive screening methods are yet available for their detection [1][2][3][4][5][6][7][8][9][10]. The automatic detection is performed by a Convolutional Neural Network (CNN) application based on the Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectra of the targeted drugs of abuse, as many spectrometers are portable and hence are appropriate for in situ screening for illicit compounds.…”