通过UPLC-HRMS/MS对新精神活性物质的查进行类类类比的软独立建模
Ilenia Bracaglia1,2, Sara Gamberoni1, Camilla Montesano1
1Department of Chemistry, Sapienza University of Rome, Rome 00185, Italy.
Analytical chemistry
|July 11, 2025
概括
本研究引入了一种使用UPLC-HRMS和多变量分析的新方法,用于识别和分类新的精神活性物质 (NPS). 开发的模型通过准确识别这些具有挑战性的化合物,有效地支持法医调查.
科学领域:
- 法医化学 法医化学
- 分析化学 分析化学
- 毒理学 毒理学 毒理学
背景情况:
- 新型精神活性物质 (NPS) 构成了全球性挑战,原因是可访问性和规避标准药物测试.
- 它们不可预测的毒性和难以识别的难度需要先进的分析方法.
研究的目的:
- 开发和验证一种可靠的方法来识别和分类未知的NPS.
- 利用UPLC-HRMS和多变量分析进行增强的NPS检测.
主要方法:
- 获取159个分析标准的双重质谱 (MS/MS) 频谱.
- 将保留时间和MS数据预处理为训练和测试套件.
- 主要组件分析 (PCA) 和软独立类类模拟建模 (SIMCA) 的应用用于分类.
主要成果:
- 虽然PCA成功地将不同的NPS类聚合在一起,但有些人表现出分散.
- 经过验证的SIMCA模型在NPS分类方面表现出高准确性,特别是在较低的碰撞能量数据中.
- 用被查获的药物样本进行的外部验证证实了模型对现实世界法医应用的可靠性.
结论:
- 联合UPLC-HRMS和多变量分析方法为NPS识别和分类提供了有效的工具.
- 对于支持涉及NPS的法医调查,SIMCA模型显示出显著的前景.
- 进一步的开发可能会提高对具有有限标准可用性的类别的分类准确性.
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