Presumptive Tests for Xylazine – A Computer Vision Approach

14 August 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The emergence of xylazine on the illicit drug market is, at the time of writ- ing, a contemporary global public health concern. Presumptive tests are one of the most common methods to qualitatively detect the presence of a drug class from an unknown powder. However, the subjectivity of by eye colour interpretation can result in false positive or false negative results. Despite the alarm of increasing abuse, there are no known presumptive test strategies to selectively detect the presence of xylazine. Herein, an experimental protocol utilising Kineticolor, a computer vision software, was developed to qualita- tively and quantitatively study the application of existing presumptive tests for xylazine detection. Mandelin, Marquis, and Mecke reagents were found to give positive test results with xylazine. The combination of these results, rather than any one in isolation, proved to be unique for xylazine versus seven other white powdered test samples (caffeine, sucrose, amphetamine, metham- phetamine, MDMA, morphine and diazepam). Using Kineticolor computer vision software, time-resolved colour change profiles were generated. Thus, beyond xylazine showing distinct colour changes in all three tests, it also evidenced characteristic and quantifiable rates of colour changes over time. These findings highlighted the potential use of the combination of Mandelin, Marquis, and Mecke reagents for detecting xylazine, and the possibility of us- ing time-resolved computer vision methods to more objectively quantify the presumptive tests. To the best of our knowledge, these findings represent the first presumptive test strategy towards specific, quantifiable, and potentially field-ready detection of xylazine.

Keywords

computer vision
imaging
forensics
illicit drugs
presumptive test
spot test
xylazine

Supplementary materials

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Description
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Machine readable supporting information
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Machine-readable files related to 3D printing CAD files, computer vision data, and NMR analysis.
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