Hierarchical cluster analysis applied for the automated recognition of psychoactive substances and of their main precursors

Ciochina, Stefanut; Praisler, Mirela; Coman, Marian

doi:10.1109/iseee.2017.8170652

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The w TE 2 feature weight is presented in Fig. 1, which shows that w TE 2 acts as a powerful selective amplifier: it increases significantly the intensity of the bands found around 1245 and 1190 cm -1 , which are the most stable and the strongest absorptions of the hallucinogenic amphetamines [6,7].…”

Section: Resultsmentioning

confidence: 99%

“…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%

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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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Section: Resultsmentioning

confidence: 99%

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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“…The spectral input consists of 19 positives and 17 negatives (non-phenethylamines, class code N). The positives are 7 stimulant amphetamines (amphetamine, analogues and homologues, class named M), 6 ephedrines (ephedrine, stereoisomers and diastereomers, class named E), 6 hallucinogenic amphetamines (3,4methylenedioxyamphetamines and analogues, class named T) [11][12][13].…”

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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AI-Driven Approaches to Reshape Forensic Practices

Singla,

Shekhar,

Ahirwar

2024

Cases on Forensic and Criminological Science for Criminal Detection and Avoidance

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Forensic investigation is ushering into a new era of transformation propelled by rapid technological developments and innovations. The criminals are getting smarter, and crimes are becoming more complex; in such a time dissemination of justice requires commensurate technological enhancement. This chapter explores the vast potential of AI in revolutionizing Forensic Science and provides a succinct overview into the applicability of artificial intelligence (AI) and machine learning (ML) to facilitate classification, characterization, discrimination, differentiation, and recognition of forensic exhibits. This chapter further delves into the fundamental principles of supervised, unsupervised, semi-supervised, and reinforcement learning approaches and describes common ML methods which are frequently employed by researchers of this field.

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Hierarchical cluster analysis applied for the automated recognition of psychoactive substances and of their main precursors

Cited by 3 publications

References 9 publications

Chemometric application operating a portable laser infrared sensor detecting illicit phenethylamines

Chemometric application operating a portable laser infrared sensor detecting illicit phenethylamines

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

AI-Driven Approaches to Reshape Forensic Practices

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