通过使用表面增强的拉曼光谱和机器学习进行眼分析来快速检测药物滥用
Yingbin Wang1, Yulong Huang2, Xiaobao Liu2
1Department of Ophthalmology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, 134 Dongjie Rd, Fuzhou, 350001, Fujian, China.
Scientific reports
|January 8, 2025
概括
这项研究提出了一种快速的,非侵入性的方法来检测眼中的 (EPH),使用滴涂层沉积表面增强的拉曼光谱学 (DCD-SERS) 和机器学习. 该技术在识别EPH方面达到90%以上的准确性,为药物滥用查提供了一个有前途的工具.
科学领域:
- 分析化学 分析化学
- 法医科学 法医科学 法医科学
- 生物医学工程 生物医学工程
背景情况:
- 滥用毒品是一个重大的全球性挑战,需要先进的检测方法.
- 目前的药物检测技术往往缺乏速度,准确性或成本效益.
- 眼液为药物分析提供了一个非侵入性矩阵.
研究的目的:
- 开发和验证一种快速的,非侵入性的方法来检测眼中的 (EPH).
- 将滴涂层沉积表面增强拉曼光谱 (DCD-SERS) 与机器学习 (ML) 结合起来,用于增强药物选.
- 评估使用眼分析用于药物滥用检测的可行性.
主要方法:
- 在Sprague-Dawley (SD) 鼠的眼液中,以弗得林 (EPH) 度使用超高性能液态色谱-并联质谱法 (UPLC-MS/MS) 来量化.
- 滴涂层沉积表面增强拉曼光谱 (DCD-SERS) 用于直接检测眼样本中的EPH.
- 使用三个机器学习算法 (LDA,PLS-DA,RF) 来分析SERS数据并对样本进行分类.
主要成果:
- UPLC-MS/MS分析显示,在注射后3小时内,大鼠眼中的平均EPH度为1235 ng/mL.
- DCD-SERS成功地确定了EPH在1001厘米-1和1242厘米-1.1的明显的拉曼峰值.
- 机器学习模型在区分EPH阳性和负样本时,达到90%以上的准确性和高AUC值 (0.98210.9911).
结论:
- 与ML相结合的DCD-SERS提供了一种快速,准确和非侵入性的方法来检测眼中的.
- 这种方法在执法和临床药物滥用查方面具有重大潜力.
- 该方法对考官来说是可访问的,在道德上是合适的,并且具有成本效益.
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