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相关概念视频

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

298
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...
298
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...
330

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Updated: Jun 14, 2025

Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets
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PyFasma:一个开源的,模块化的Python包,用于预处理和多变量分析拉曼光谱数据.

Eleftherios Pavlou1, Nikolaos Kourkoumelis1

  • 1Department of Medical Physics, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece. nkourkou@uoi.gr.

The Analyst
|June 13, 2025
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概括
此摘要是机器生成的。

PyFasma是一个新的Python包,用于分析生物样本的拉曼光谱数据. 它通过识别关键的生化差异,有助于区分健康和患病的骨.

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科学领域:

  • 生物医学光谱学 生物医学光谱学
  • 计算生物学 计算生物学
  • 数据科学数据科学数据科学

背景情况:

  • 拉曼光谱为生物样本提供无标签的分子分析.
  • 高通量光谱数据需要先进的预处理和分析来检测微妙的特征.

研究的目的:

  • 介绍PyFasma,一个开源的Python包用于拉曼光谱数据分析.
  • 为可重复的光谱解释提供一个用户友好的,可扩展的框架.

主要方法:

  • PyFasma集成了预处理 (除,平滑,基线校正,规范化),缩小维度 (PCA,PLS-DA) 和解卷.
  • 使用一个模块化,Jupyter笔记本友好的框架.
  • 采用重复分层交叉验证来进行可靠的模型验证.

主要成果:

  • 在一个比较健康和骨质疏松性皮层骨的案例研究中证明了PyFasma的实用性.
  • 确定了矿物与矩阵比率和结晶度的统计学上显著差异.
  • 通过使用PCA和PLS-DA,成功地实现了健康和病态骨谱之间的区别.

结论:

  • 在生物研究中,PyFasma为复杂的拉曼光谱分析提供了强大且易于使用的解决方案.
  • 提高了光谱数据中的多变量分析的概括性和可重复性.
  • 使用拉曼光谱来解释疾病状态中的生化差异.