根据变压器和手动注释的数据估计拉曼光谱的基线.
Jiangsan Zhao1, Tomasz Woznicki2, Krzysztof Kusnierek1
1Department of Agricultural Technology, Center for Precision Agriculture, Norwegian Institute of Bioeconomy Research (NIBIO), Nylinna 226 2849, Kapp, Norway.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|December 29, 2024
概括
深度学习有效地纠正了拉曼光谱数据中的基线. 一个新的变压器模型 (1dTrans) 超越了传统方法,改善了生物组织的光谱分析.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 生物物理学的生物物理.
背景情况:
- 拉曼光谱对于分析生物组织至关重要.
- 基线和噪声干扰阻碍了精确的光谱分析.
- 当前的基线校正方法通常是手动的,耗时的.
研究的目的:
- 为拉曼光谱开发一种自动化和准确的基线校正方法.
- 对传统方法进行深度学习方法的评估.
- 增强用于生物应用的拉曼光谱数据的预处理.
主要方法:
- 对于八种生物材料,手工策划的基本真相基线.
- 修改多多项式适配 (Modpoly),改进修改多多项式适配 (IModpoly) 和airPLS方法的调参数.
- 设计和实施一维变压器 (1dTrans) 模型用于基线估计.
- 与卷积神经网络 (CNN) 和ResUNet模型进行比较分析.
主要成果:
- 1dTrans模型在基线校正中表现出卓越的性能.
- 较低的平均绝对误差 (MAE) 和光谱角度映射器 (SAM) 评分是通过1dTrans.
- 深度学习模型在各种生物材料中被证明是有效的.
结论:
- 1dTrans模型为拉曼光谱基线校正提供了一个多功能和有效的解决方案.
- 自动化深度学习方法可以克服传统参数方法的局限性.
- 这一进步使得使用拉曼光谱学对生物样品进行更可靠的定量分析.
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