RSPSSL:一种新的高保真拉曼光谱预处理方案,用于增强生物医学应用和化学分辨率可视化
Jiaqi Hu1, Gina Jinna Chen2, Chenlong Xue1
1State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China.
Light, science & applications
|February 19, 2024
概括
一个新的自主监督学习方案 (RSPSSL) 有效预处理拉曼光谱,显著减少噪音并提高生物医学应用的准确性,如癌症诊断和代谢分析.
科学领域:
- 频谱学是一种光谱学.
- 计算生物学 计算生物学
- 生物医学工程 生物医学工程
背景情况:
- 拉曼光谱为精密医学中的材料分析和代谢分析提供分子指纹.
- 由于环境,仪器和样本噪声,在准确地归因光谱峰值方面存在挑战.
- 有效的光谱预处理对于从拉曼数据中提取有价值的信息至关重要.
研究的目的:
- 引入一种基于自主监督学习 (RSPSSL) 的新型拉曼光谱预处理方案.
- 为了证明RSPSSL的高容量,光谱精度和快速处理速度.
- 展示RSPSSL在增强各种生物医学应用中的有效性.
主要方法:
- 基于自主监督学习 (SSL) 的拉曼光谱预处理方案 (RSPSSL) 的开发.
- 在没有重新训练的情况下,RSPSSL处理任意的拉曼光谱,实现高速 (~1900光谱/秒).
- 使用实验数据对RSPSSL的性能与既定技术进行评估.
主要成果:
- RSPSSL实现了88%的根平均平方误差减少和60%的无限规范减少.
- 在生物医学应用中观察到显著的准确性改善:在癌症诊断中400% (ΔAUC),38% (几次射击) 和242%在帕拉克瓦特预测中.
- 在生物医学高光谱图像中提高化学分辨率,特别是在光谱指纹区域.
结论:
- RSPSSL为拉曼光谱预处理提供了一个强大,高效和多功能工具.
- 该方案展示了卓越的容量,信号保真性和在各种数据集和设备上广泛的适用性.
- RSPSSL 能够实现先进的无标签分子成像,用于高通量代谢和生物医学机制查.
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