在社区药物检查中实现自动化红外光谱分析
Lea Gozdzialski1, Abby Hutchison2,3, Bruce Wallace2,4
1Department of Chemistry, University of Victoria, Victoria, British Columbia, Canada.
Drug testing and analysis
|May 30, 2023
概括
使用随机森林模型对红外光谱的自动解释可以改善药物检查服务. 这种方法提高了速度和准确性,通过克服服务获取障碍来帮助减少伤害的努力.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 法医科学 法医科学 法医科学
背景情况:
- 红外光谱分析对于识别药物混合物至关重要,药物检查服务在全球范围内不断扩大.
- 人类对光谱数据的解释是耗时的,需要专门的专业知识.
- 速度,准确性和可访问性等障碍限制了对药物检查服务的参与.
研究的目的:
- 开发和验证使用红外光谱检查药物检查的自动化解释模型.
- 评估随机森林模型在检测MDA和芬等特定化合物的性能.
- 在减少危害的背景下建立计算机辅助解释和验证的工作流程.
主要方法:
- 一个随机的森林模型被训练并优化,使用来自社区药物检查站点收集的药物样本的红外光谱.
- 模型预测在各种药物混合物上得到了验证,包括含有MDMA,芬太尼和芬太尼的药物混合物.
- 形状增量解释 (SHAP) 和k-最近邻居用于模型解释性和视觉比较.
主要成果:
- 该模型在MDA检测方面实现了79%的真正阳性和100%的真负率.
- 对于芳,该模型显示了61%的真正阳性和97%的真负率.
- 在各种样本混合物中成功检测了MDA和芳.
结论:
- 红外光谱的自动解释显示了提高药物检查服务的效率和可访问性的前景.
- 计算机辅助口译可以帮助克服服务参与的障碍,支持减少伤害的倡议.
- 建立对人工智能驱动的光谱分析的信任是促进其在现实世界药物检查环境中采用的关键.
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