Artificial intelligence application designed to screen for new psychoactive drugs based on their ATR-FTIR spectra

Convolutional Neural Network Detecting Synthetic Cannabinoids

“…As opposed to other forensic screening systems built with ATR-FTIR spectra, the presented CNN) system operates in a simpler (and hence faster) way, as no variable selection is needed before the classification itself [9]. We must also stress that these performances were obtained without focusing on the important spectral regions (the infrared fingerprint region), which is usually a crucial step in the analysis of vibrational spectroscopic data.…”

“…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.…”

Automatic identification of hallucinogenic amphetamines based on their ATR-FTIR spectra processed with Convolutional Neural Networks

“…The ATR-FTIR spectra may be easier and faster to record than the GC-FTIR spectra, but they have a significantly weaker intensity than the later. Secondly, as opposed to other systems built with ATR-FTIR spectra [12], the CNN system operates in a simpler (and hence faster) way, as no variable selection is needed before the classification itself. Last but not the least, the CNN system was built with a complex architecture, aiming to test simultaneously the class identity of an unknown against two classes of positives, i.e.…”

“…Hence, the efficiency of the CNN model has been assessed in comparison with other artificial intelligence applications screening for drugs of abuse that we have formerly developed, e.g. Partial Least Squares Regression (PLSR) [2], Partial Least Squares Discriminant Analysis coupled with Genetic Algorithms (PLS-DA) [3,12], and class modelling solutions, such as Soft Class Analog Independent Modelling (SIMCA) [4][5][6], Principal Component Analysis (PCA) [7,8] and Artificial Neural Networks (ANNs) [9][10][11].…”

Improved detection of 2C-x and Dox amphetamines – an analytical tool mitigating the environmental impact of their illicit manufacturing, consumption and disposal

“…The samples belong to three sets of drugs: 2C-x and DOx psychedelic amphetamines (class 1), cannabinoids (class 2), and negatives (class 3), the latter being other substances of forensic interest that have been randomly selected. A second dataset, generated by selecting the most important 186 features by applying a Genetic Algorithm (GA), has been used for analysis and comparison [4].…”