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使用纸喷雾质谱仪对伪造的非法阿片类药物进行支持向量机分类

Allie Miskulin¹, Bruce Wallace^2,3, Chris G Gill^1,3,4,5

¹Department of Chemistry, University of Victoria, Victoria, British Columbia V8W 3V6, Canada.

Analytical chemistry

|December 26, 2025

概括

新的药物检查方法使用机器学习来检测非法阿片类药物样本中的未知芬太尼 adulterants. 这种方法通过识别药物供应中的新兴物质来提高公共安全.

科学领域:

法医化学法医化学
分析化学分析化学
数据科学数据科学数据科学

背景情况:

北美非法阿片类药物供应复杂而强大,对吸毒者构成风险.
目前的药物检查方法,如有针对性的纸喷雾质谱 (PS-MS),难以检测新型精神活性物质.
非法药物中的新兴杂物需要先进的分析技术来及时识别.

研究的目的:

开发和验证一种机器学习分类器,用于检测阿片类药物样本中的未知造物.
利用支持矢量机 (SVM) 算法来识别超越常规目标分析的物质.
提高药物检查能力,以应对非法药物供应中新出现的威胁.

主要方法:

采用支持矢量机 (SVM) 分类器,采用全扫描质谱数据.
使用纸喷雾质谱仪 (PS-MS) 进行快速的现场药物分析.
应用形状增量解释 (SHAP) 来解释SVM模型的预测.

主要成果:

SVM分类器在芬太尼和咖啡因混合物中成功检测到新兴的伪造剂ortho-methylfentanyl.
实现了0.77的精度和0.94的回忆,用于检测 орто-甲基芬太尼.
SHAP分析提供了有关驱动伪造剂识别的化学特征的见解.

结论:

开发的SVM方法在常规药物分析过程中有效地从质谱数据中识别 adulterants.
这种方法提高了药物检查程序检测新药和新兴物质的能力.
这些发现为改善非法毒品供应安全提供了新的策略.

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