3D-printed portable device for illicit drug identification based on smartphone-imaging and artificial neural networks

J Cruz; R Sáez-Hernández; S Armenta; A E Morales-Rubio; M L Cervera

doi:10.1016/j.talanta.2024.126217

3D-printed portable device for illicit drug identification based on smartphone-imaging and artificial neural networks

Talanta. 2024 May 6:276:126217. doi: 10.1016/j.talanta.2024.126217. Online ahead of print.

Authors

J Cruz¹, R Sáez-Hernández², S Armenta², A E Morales-Rubio², M L Cervera²

Affiliations

¹ EUSS School of Engineering, Pg. Sant Joan Bosco, 74, 08017, Barcelona, Spain. Electronic address: jcruz@euss.cat.
² Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, 46100, Burjassot, Valencia, Spain.

PMID: 38759361
DOI: 10.1016/j.talanta.2024.126217

Abstract

In this manuscript, a 3D-printed analytical device has been successfully developed to classify illicit drugs using smartphone-based colorimetry. Representative compounds of different families, including cocaine, 3,4-methylenedioxy-methamphetamine (MDMA), amphetamine and cathinone derivatives, pyrrolidine cathinones, and 3,4-methylenedioxy cathinones, have been analyzed and classified after appropriate reaction with Marquis, gallic acid, sulfuric acid, Simon and Scott reagents. A picture of the colored products was acquired using a smartphone, and the corrected RGB values were used as input data in the chemometric treatment. ANN using two active layers of nodes (6 nodes in layer 1 and 2 nodes in layer 2) with a sigmoidal transfer function and a minimum strict threshold of 0.50 identified illicit drug samples with a sensitivity higher than 83.4 % and a specificity of 100 % with limits of detection in the microgram range. The 3D printed device can operate connected to a rechargeable lithium-ion cell portable battery, is inexpensive, and requires minimal training. The analytical device has been able to discriminate the analyzed psychoactive substances from cutting and mixing agents, being a useful tool for law enforcement agents to use as a screening method.

Keywords: Colorimetric tests; Deep learning algorithms; Drug classification; Portable analytical devices; Screening procedures; Smartphone-based colorimetry.