适应物理意识拉曼基线校正与机器学习预测参数
Prasad D Aradhye1,2, Souparna Mandal3, Robert D Gray4
1EaStCHEM School of Chemistry, The University of Edinburgh, King's Buildings, Mayfield Road, Edinburgh EH9 3FJ, U.K.
Analytical chemistry
|November 26, 2025
概括
一种名为DIRAS (Dynamic Iterative Reweighted Autoregressive Spectral baseline correction) 的新方法及其深度学习扩展DIRAS+提供自动,准确的拉曼光谱基线校正,无需手动调节,改进数据分析.
科学领域:
- 频谱学是一种光谱学.
- 化学测量 化学测量 化学测量
- 机器学习 机器学习
背景情况:
- 准确的基线校正对于解释拉曼光谱至关重要.
- 现有的方法在自动化,通用性和用户控制方面存在局限性.
研究的目的:
- 开发一种自动,自适应的拉曼光谱基线校正方法.
- 为了提高高通量应用的基线校正的稳定性和准确性.
主要方法:
- 开发了DIRAS (动态代重权自行回归光谱基线校正),具有固定的规范化参数 (λ).
- 使用结构相似性指数测量 (SSIM) 来优化λ.
- 训练了一种深度学习模型 (DIRAS+),用于实时,频谱特定的 λ 预测.
主要成果:
- 在SERS数据集上,DIRAS+在ALS和SEALS上表现优越.
- 实现了更好的峰值保真度,减少了类内变化,并将基线扭曲最小化.
- 迪拉斯提高了校准和化学测量模型的性能,提高了分析灵敏度.
结论:
- 迪拉斯和迪拉斯+为拉曼光谱学提供了强大,可扩展和用户适应的解决方案.
- 这些方法自动化了基线校正,这对于高通量光谱分析至关重要.
- 迪拉斯+可实现实时,频谱特定的基线校正,推进光谱解释.
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