优化的机器学习方法将表面增强的拉曼散射和红外数据结合起来,用于在非法阿片类药物中检测西拉的痕迹
Rebecca R Martens1, Lea Gozdzialski1, Ella Newman1
1Department of Chemistry, University of Victoria, Victoria, British Columbia, V8W 3V6, Canada. dkhore@uvic.ca.
The Analyst
|January 21, 2025
概括
结合红外吸收光谱和使用数据融合的表面增强拉曼光谱,可以显著改善非法阿片类药物中西拉的检测. 高级融合与随机森林分类实现了96%的灵敏度,用于准确的护理点分析.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 化学测量 化学测量 化学测量
背景情况:
- 非法阿片类药物样本通常含有诸如西拉之类的杂物,使检测和治疗复杂化.
- 准确和快速检测西拉对于公共卫生和法医分析至关重要.
- 整合多种光谱技术可以克服单个方法的局限性.
研究的目的:
- 为了提高在非法阿片类药物样本中检测西拉的预测准确度.
- 评估不同数据融合策略 (混合,中级,高级) 与化学测量模型相结合的有效性.
- 在复杂矩阵中确定敏感和特异性克西拉识别的最佳方法.
主要方法:
- 获得了红外吸收光谱和表面增强的拉曼光谱数据.
- 实施了三种数据融合策略:混合 (光谱连接),中级 (特征提取) 和高级 (预测融合).
- 采用随机森林,支持向量机和k-最近邻近算法,并通过5倍交叉验证网格搜索进行优化.
主要成果:
- 随机森林分类器在所有融合策略中显示出最佳性能.
- 高级数据融合实现了最高的灵敏度 (96%) 和特异性 (88%),F1得分为92%.
- 高级聚变方法有效地利用表面增强的拉曼数据 (90%的投票权) 与红外光谱数据,保持高预测准确度 (92%).
结论:
- 红外吸收光谱和表面增强的拉曼光谱的数据融合显著提高了非法阿片类药物样本中西拉检测的准确性.
- 高级融合策略,利用随机森林分类,为分析复杂的阿片类药物配方提供了强大而准确的方法.
- 这种多种仪器的方法显示出对点护理应用的希望,使药物样本中改剂的更好识别成为可能.
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