在NIR和MIR地区使用基于MEMS的FTIR光谱和机器学习快速无试剂COVID19检测
Ahmed Abdelkhalik1,2, Mazen Erfan3,4, Bassem Mortada1
1Si-Ware Systems, 3 Khaled Ibn El-Waleed Street, Heliopolis, Cairo, Egypt.
Scientific reports
|October 7, 2025
概括
快速,无试剂的COVID-19检测是通过小型化的富里埃变换红外光谱仪 (FTIR) 和机器学习实现的. 这种新的方法为传染病提供了快速的,在治疗地点进行的检测.
科学领域:
- 频谱学是一种光谱学.
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 准确和快速的COVID-19检测对公共卫生至关重要.
- 目前的诊断方法可能耗时或需要专门的设施.
- 为及时诊断和管理,需要治疗点检测 (POC) 解决方案.
研究的目的:
- 开发和验证用于COVID-19检测的快速,无试剂的方法.
- 将小型化的基于MEMS的FTIR光谱仪与用于光谱分析的机器学习相集成.
- 为了评估NIR和MIRFTIR光谱检测COVID-19的诊断性能.
主要方法:
- 使用两种便携式FTIR光谱仪 (NIR和MIR) 分析了363个鼻拭片样本.
- 通过间隔部分最小平方差分分析 (iPLS-DA) 进行光谱数据预处理和分析.
- 使用蒙特卡洛交叉验证的模型培训和评估.
主要成果:
- 湿样MIR模型的准确度达到了79%,灵敏度达到了98%,AUC为0.8.8.
- 该MIR干样模型显示80%的准确性和AUC为0.79.
- 该NIR模型实现了66%的准确性和0.64.6的AUC.
- 在胺A/B和C-H外调区域中确定了关键的光谱特征.
- 总测量时间不到6分钟.
结论:
- 微型FTIR光谱仪与机器学习相结合,为COVID-19提供了快速,无试剂的诊断方法.
- 开发的方法表明了实时,医疗点测试的潜力.
- 进一步的优化可能会提高诊断准确度,以广泛的临床应用.
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