弥合光谱差距:在基于血液的红外光谱学中跨设备模型概括
Flora B Nemeth1,2,3, Niklas Leopold-Kerschbaumer2,3, Diana Debreceni1
1Center for Molecular Fingerprinting (CMF), 1093 Budapest, Hungary.
Analytical chemistry
|May 7, 2025
概括
这项研究引入了一种数据增强方法,以改进机器学习模型对不同设备的血液红外光谱的概括. 该技术在福利埃变换红外光谱仪器 (FTIR) 之间传输模型时提高了模型的准确性和可靠性.
科学领域:
- 频谱学是一种光谱学.
- 机器学习 机器学习
- 生物医学工程 生物医学工程
背景情况:
- 血液红外光谱正在获得分析的普及.
- 机器学习模型通常因设备变异而难以跨设备概括.
- 确保不同工具的模型性能对于广泛采用至关重要.
研究的目的:
- 开发一种改进血液红外光谱学跨设备模型概括的方法.
- 提高机器学习模型对独特设备特征的适应性.
- 为了验证一个新的领域适应技术.
主要方法:
- 一种使用数据增强的简单域调整方法.
- 将特定于设备的差异纳入增强训练数据.
- 在两个不同的富里埃变换红外光谱仪器 (FTIR) 上进行实验验证.
主要成果:
- 拟议的数据增强方法显著提高了预测准确度.
- 当应用于不同的FTIR设备时,增强了模型可靠性.
- 证明有效地适应设备间的变化.
结论:
- 将设备特定差异纳入数据增强是血液红外光谱学中跨设备概括的有效策略.
- 该方法增强了机器学习模型在这个领域的实际实用性.
- 这种方法为在不同的实验室环境中部署光谱模型提供了可行的解决方案.
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