JOURNAL ARTICLE
Performance of Hair Testing for Cocaine Use—Comparison of Five Laboratories Using Blind Reference Specimens.
Published In: Journal of Analytical Toxicology, 2023, v. 47, n. 2. P. 154 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Hart, E Dale; Vikingsson, Svante; Winecker, Ruth E; Evans, Amy L; Cone, Edward J; Mitchell, John M; Hayes, Eugene D; Flegel, Ronald R 3 of 3
Abstract
This article focuses on a comparative study of five commercial hair testing laboratories analyzing cocaine in workplace drug testing, evaluating their bias, precision, selectivity, and decontamination efficiency. Using nine blind hair specimens—including cocaine-positive samples, some contaminated with methamphetamine, and negative samples contaminated with cocaine powder—the study found that all laboratories correctly identified cocaine in drug user specimens but showed substantial interlaboratory variability in quantitative results, with up to fivefold differences likely due to method-dependent factors such as decontamination and extraction protocols. None of the laboratories completely removed external cocaine contamination, indicating current standards may not fully distinguish drug use from contamination; however, metabolite-to-cocaine ratios, particularly of hydroxylated cocaine metabolites, showed potential for differentiating these cases. The findings highlight challenges in standardizing hair drug testing and suggest that proficiency testing programs could improve laboratory performance over time.
Additional Information
- Source:Journal of Analytical Toxicology. 2023/03, Vol. 47, Issue 2, p154
- Document Type:Article
- Subject Area:Law
- Publication Date:2023
- ISSN:0146-4760
- DOI:10.1093/jat/bkac066
- Accession Number:162589572
- Copyright Statement:Copyright of Journal of Analytical Toxicology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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