A Deep Learning Approach to Investigating Clandestine Laboratories Using a GC-QEPAS Sensor

Author:

Felizzato Giorgio1ORCID,Liberatore Nicola2ORCID,Mengali Sandro2ORCID,Viola Roberto2ORCID,Moriggia Vittorio3ORCID,Romolo Francesco Saverio1

Affiliation:

1. Department of Law, University of Bergamo, Via Moroni 255, 24127 Bergamo, Italy

2. Consorzio CREO, 67100 L’Aquila, Italy

3. Department of Economics, University of Bergamo, Via dei Caniana 2, 24127 Bergamo, Italy

Abstract

Illicit drug production in clandestine laboratories involves the use of large quantities of different chemicals that can be obtained for legitimate purposes. The identification of these chemicals, including reagents, catalyzers and solvents, is crucial for forensic investigations. From a legal point of view, a drug precursor is a material that is specific and critical to the production of a finished chemical and that constitutes a significant portion of the final molecular structure of the drug. In this study, a gas chromatography quartz-enhanced photoacoustic spectroscopy (GC-QEPAS) sensor—in conjunction with a deep learning model—was evaluated for its effectiveness in the detection and identification of interesting compounds for the production of amphetamine, methamphetamine, 3,4-methylenedioxymethamphetamine (MDMA), phenylcyclohexyl piperidine (PCP), and cocaine. The GC-QEPAS sensor includes a gas sampler, a fast GC for separation, and a QEPAS detector, which excites molecules exiting the GC column using a quantum cascade laser to provide the infra-red (IR) spectrum. The on-site capability of the GC-QEPAS system offers significant advantages over the current instruments employed in this field, including rapid analysis, which is crucial in field operations. This allows law enforcement to quickly identify specimens of interest on site. The system’s performance was validated by taking into account the limit of detection, repeatability, and within-laboratory reproducibility. The results showed excellent repeatability and reproducibility for both the GC and QEPAS modules. The deep learning model, a multilayer perceptron neural network, was trained using IR spectra and retention times, achieving very high classification accuracy in the testing conditions. This study demonstrated the efficacy of the GC-QEPAS sensor combined with a deep learning model for the reliable identification of drug precursors, providing a robust tool for law enforcement during criminal investigations in clandestine laboratories.

Funder

HORIZON2020, RISEN project

Publisher

MDPI AG

Reference38 articles.

1. Tilstone, W.J., Hastrup, M.L., and Hald, C. (2019). Fisher Techniques of Crime Scene Investigation First International Edition, CRC Press.

2. Christian, D.R. (2022). Forensic Investigation of Clandestine Laboratories, CRC Press.

3. United Nations Office on Drugs and Crime (2019). The International Drug Control Conventions: Tables of the United Nations Convention against Illicit Traffic in Narcotics Drugs and Psychotropic Substances of 1988.

4. United Nations Office on Drugs and Crime (2013). World Drug Report 2013, United Nations Office on Drugs and Crime.

5. (2023). COUNCIL REGULATION (EC) No 111/2005 of 22 December Laying Down Rules for the Monitoring of Trade between Precursors 2004 the Community and Third Countries in Drug Precursors.

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