JOURNAL ARTICLE
Optimization of a disposable pipette tips extraction for the analysis of psychoactive substances in sweat specimens using design of experiments.
Published In: Journal of Analytical Toxicology, 2025, v. 49, n. 2. P. 104 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Gomes, Nayna Cândida; Bigão, Vítor Luiz Caleffo Piva; Campos, Eduardo Geraldo de; Cabrices, Oscar; Costa, Bruno Ruiz Brandão da; Martinis, Bruno Spinosa De 3 of 3
Abstract
This article focuses on the development and optimization of extraction methods using disposable pipette tips extraction (DPX), a miniaturized solid-phase extraction technique, for detecting conventional drugs of abuse and novel psychoactive substances (NPS) in sweat samples via gas chromatography–mass spectrometry (GC–MS). The study employed Design of Experiments (DoE) to optimize parameters such as sample volume, solvent volumes, aspiration times, and extraction cycles for both basic and neutral psychoactive compounds, achieving improved analyte recoveries (54.7% increase for basic and 39.2% for neutral compounds). The optimized DPX methods enable simultaneous extraction of multiple substances, including amphetamines, cocaine, cannabinoids, fentanyl, and various NPS, from sweat, highlighting sweat as a valuable, non-invasive biological matrix for clinical and forensic toxicology. While the study demonstrates the feasibility and efficiency of DPX for sweat analysis, it notes that further validation is required to confirm the method's performance for qualitative and quantitative applications.
Additional Information
- Source:Journal of Analytical Toxicology. 2025/03, Vol. 49, Issue 2, p104
- Document Type:Article
- Subject Area:Science
- Publication Date:2025
- ISSN:0146-4760
- DOI:10.1093/jat/bkae090
- Accession Number:183076447
- 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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