Cluster analysis evaluating the automated detection of drugs of abuse with a new hollow fiber based quantum cascade laser infrared spectrometer

Ciochina, Stefanut; Praisler, Mirela; Negoiță, Cătălin

doi:10.1109/ehb.2017.7995405

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…The database consists of the infrared spectra of 36 compounds, recorded between 1405 and 1150 cm -1 , which is the domain where the UT8 quantum cascade laser (QCL) that equipps the portable spectrometer is emitting. The absorption has been measured every 5 cm -1 [9,10]. The spectral input consists of 19 positives and 17 negatives (non-phenethylamines, class code N).…”

Section: Experimental Partmentioning

confidence: 99%

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

Praisler

Ciochina

2019

Ann_UGAL_Math_Phys_Mec

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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.

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Section: Experimental Partmentioning

confidence: 99%

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

Praisler

Ciochina

2019

Ann_UGAL_Math_Phys_Mec

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“…The 36 spectra included in the database have been recorded in the spectral domain (1405 -1150 cm -1 ), specific to its quantum cascade laser (QCL) source of electromagnetic radiation (UT8), with a resolution of 5 cm -1 [4,5]. The database contains the spectra of 7 stimulant amphetamines (amphetamine and its main analogues and homologues, assigned class code M), 6 ephedrines (ephedrine and its main stereoisomers and diastereomers, assigned class code E), 6 hallucinogenic amphetamines (3,4methylenedioxyamphetamines and its main analogues, assigned class code T) and 17 negatives (nonphenethylamines, class code N) [6][7][8].…”

Section: Experimental Partmentioning

confidence: 99%

Chemometric application operating a portable laser infrared sensor detecting illicit phenethylamines

Ciochina

Praisler

2019

Ann_UGAL_Math_Phys_Mec

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We are presenting a chemometrical application designed to recognize compounds having a molecular structure similar to the main controlled stimulant and hallucinogenic illicit phenetylamines, i.e. to amphetamines and their main precursors, the ephedrines. The input database contains their infrared laser spectra, which have been recorded with a new portable GC -IR sensor, in the spectral domain (1405 -1150 cm -1 ), specific to its quantum cascade laser (QCL) source of electromagnetic radiation (UT8). A discriminating w TE feature weight was first determined by using the spectra of the targeted positives and the spectra of various compounds of forensic interest. The input database, formed with the w TE 2 preprocessed laser infrared spectra, has been subjected to Principal Component Analysis (PCA). The scores plots have been analyzed in order to evaluate to what extent the amphetamines and their main precursors, the ephedrines, are forming specific and successfully distinguishable clusters. The results indicate that the application meets the requirements of forensic analytical tools.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Detecting Synthetic Cannabinoids

Burlacu

Gosav

Burlacu

et al. 2021

2021 International Conference on E-Health and Bioengineering (EHB)

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We are presenting a convolutional neural network (CNN) application recognizing the class identity of synthetic cannabinoids based on their Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectra. The results indicate that this CNN system can be efficiently used to distinguish JWH synthetic cannabinoids from other substances of forensic interest, but also from other types of synthetic cannabinoids. One of the main advantages of the system is that it can also operate on mobile ATR-FTIR spectrometers used in field operations.

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Cluster analysis evaluating the automated detection of drugs of abuse with a new hollow fiber based quantum cascade laser infrared spectrometer

Cited by 8 publications

References 8 publications

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

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

Chemometric application operating a portable laser infrared sensor detecting illicit phenethylamines

Convolutional Neural Network Detecting Synthetic Cannabinoids

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