JOURNAL ARTICLE

Development and Validation of a GC–MS-EI Method to Determine α-PHP in Blood: Application to Samples Collected during Medico-Legal Autopsies.

  • Published In: Journal of Analytical Toxicology, 2023, v. 47, n. 3. P. 271 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Machado, Francisca; Franco, João; Vieira, Duarte Nuno; Margalho, Cláudia 3 of 3

Abstract

This article focuses on the development and validation of an analytical method for detecting and quantifying alpha-pyrrolidinohexanophenone (α-PHP), a synthetic cathinone and new psychoactive substance (NPS), in blood samples using solid-phase extraction (SPE) combined with gas chromatography–mass spectrometry–electron ionization (GC–MS–EI). The method demonstrated high selectivity, linearity (10–1,000 ng/mL), sensitivity (limit of detection 5 ng/mL; limit of quantitation 10 ng/mL), precision, and stability, and was successfully applied to 15 postmortem cases from Portugal, revealing α-PHP concentrations ranging from 15 to 227 ng/mL alongside various other drugs. The study highlights the increasing presence of α-PHP in forensic toxicology despite low overall NPS consumption in Portugal and emphasizes the method's utility for routine casework, especially in laboratories lacking more advanced instrumentation. Additionally, the procedure allows differentiation between α-PHP and its structural isomer α-pyrrolidinoisohexanophenone (α-PiHP), contributing to more accurate forensic analyses.

Additional Information

  • Source:Journal of Analytical Toxicology. 2023/04, Vol. 47, Issue 3, p271
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2023
  • ISSN:0146-4760
  • DOI:10.1093/jat/bkac104
  • Accession Number:162674318
  • 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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