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Raman Spectroscopy Instrumentation: Overview01:26

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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
292
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
298

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低成本的拉曼光谱设置与机器学习模型相结合.

Catarina Domingos1, Alessandro Fantoni1,2, Miguel Fernandes1,2

  • 1Department of Electronics, Telecommunication and Computers, Lisbon School of Engineering (ISEL), Polytechnic University of Lisbon (IPL), Rua Conselheiro Emídio Navarro, n°1, 1959-007 Lisbon, Portugal.

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研究人员开发了一种便携式,低成本的拉曼光谱系统,用于疾病诊断. 这种负担得起的系统分析尿液样本,为护理点应用和早期疾病风险评估提供了潜力.

关键词:
拉曼光谱法 拉曼光谱法 拉曼光谱法诊断 诊断 诊断 诊断 诊断 诊断仪器仪表仪表仪器仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪表仪器仪表仪表仪表仪表仪表仪器仪表仪器仪表仪表仪表仪器仪表仪表仪表仪表仪表仪表仪表仪表仪表仪器仪表仪表仪表仪表仪表仪器仪表仪器仪表仪器仪表仪器仪表脏疾病 脏疾病护理点的护理点.传感器 传感器 传感器

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科学领域:

  • 分析化学 分析化学
  • 生物医学工程 生物医学工程
  • 频谱学是一种光谱学.

背景情况:

  • 由于不可靠的生物标志物和复杂的实验室测试,诊断病具有挑战性.
  • 拉曼光谱为分析尿液等生物流体提供了潜力,但面临着可访问性和复杂性问题.
  • 目前的拉曼光谱分析是密集和耗时的.

研究的目的:

  • 开发一种便携式,简化,低成本的拉曼光谱系统,用于复杂的液体样本分析.
  • 使用OpenRAMAN项目的方法来优化系统.
  • 为了验证系统在尿样分析和光谱分类方面的性能.

主要方法:

  • 开发了一个便携式,低成本的拉曼系统,通过激光温度和软件获取参数调整进行优化.
  • 通过从五个尿样中获取拉曼光谱来验证该系统.
  • 使用甲醇和乙醇溶液设计和训练神经网络,优化超参数以获得准确性.

主要成果:

  • 该系统表现出一致性和对尿样组成变化的敏感性.
  • 经过训练的神经网络在分类简单的拉曼光谱方面实现了99.19%的准确性和99.21%的精度.
  • 系统的开发和培训过程是高效的,神经网络的培训时间为3分钟.

结论:

  • 开发的拉曼系统是负担得起的和便携式的,适合于点的护理应用程序.
  • 这项技术简化了在临床环境之外的疾病风险评估.
  • 为了进行全面的生物标志物分析,需要进一步验证和集成先进功能.