基于深度学习的表面增强拉曼光谱的跨设备标准化,用于增强细菌识别
Sakib Mahmud1, Faizul Rakib Sayem2, Manal Hassan3
1Department of Electrical Engineering, College of Engineering, Qatar University, Doha 2713, Qatar.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|September 24, 2025
概括
这项研究引入了一种深度学习框架,用于使用表面增强拉曼光谱 (SERS) 改进病原体检测. 该技术提高了便携式设备的光谱质量,以便在护理点进行可靠,快速的识别.
科学领域:
- 频谱学是一种光谱学.
- 机器学习 机器学习
- 生物技术是生物技术.
背景情况:
- 表面增强拉曼光谱 (SERS) 提供无标签的病原体检测,但在临床诊断方面面临挑战.
- 不一致的光谱质量,糟糕的可重现性和有限的机器学习通用性阻碍了SERS的采用.
- 这些局限性阻碍了在护理点可靠,快速的病原体识别.
研究的目的:
- 开发一个深度学习框架,以提高来自便携式设备的SERS光谱质量.
- 提高机器学习模型对病原体分类的准确性和通用性.
- 为了使可靠的,实时的病原体识别在护理点.
主要方法:
- 使用便携式和实验室级拉曼系统从20个分析类收集了SERS光谱.
- 开发了SERS-D2DNet,一个序列到序列的网络,以将便携式SERS频谱转换为实验室等级的等价物.
- 实现了SuperRaman,一个轻量级的超操作神经网络,用于多类细菌分类.
主要成果:
- SERS-D2DNet显著改善了光谱质量,将平均绝对误差降至0.01,并将R2增加到98%以上.
- 在光谱转换后,SuperRaman实现了高达100%的分类准确性.
- 结合的框架显示出高于现有方法的性能,具有紧的足迹和快速推断时间.
结论:
- 拟议的深度学习框架弥合了便携式和实验室级SERS系统之间的性能差距.
- 这种可扩展的实时解决方案有助于快速检测败血症和病原体识别.
- 这项技术非常适合于便携式部署,从而推进临床诊断.
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