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相关概念视频

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

1.0K
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...
1.0K
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

1.3K
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...
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相关实验视频

Updated: Jan 10, 2026

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
09:32

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Published on: September 26, 2019

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适应物理意识拉曼基线校正与机器学习预测参数.

Prasad D Aradhye1,2, Souparna Mandal3, Robert D Gray4

  • 1EaStCHEM School of Chemistry, The University of Edinburgh, King's Buildings, Mayfield Road, Edinburgh EH9 3FJ, U.K.

Analytical chemistry
|November 26, 2025
PubMed
概括
此摘要是机器生成的。

一种名为DIRAS (Dynamic Iterative Reweighted Autoregressive Spectral baseline correction) 的新方法及其深度学习扩展DIRAS+提供自动,准确的拉曼光谱基线校正,无需手动调节,改进数据分析.

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科学领域:

  • 频谱学是一种光谱学.
  • 化学测量 化学测量 化学测量
  • 机器学习 机器学习

背景情况:

  • 准确的基线校正对于解释拉曼光谱至关重要.
  • 现有的方法在自动化,通用性和用户控制方面存在局限性.

研究的目的:

  • 开发一种自动,自适应的拉曼光谱基线校正方法.
  • 为了提高高通量应用的基线校正的稳定性和准确性.

主要方法:

  • 开发了DIRAS (动态代重权自行回归光谱基线校正),具有固定的规范化参数 (λ).
  • 使用结构相似性指数测量 (SSIM) 来优化λ.
  • 训练了一种深度学习模型 (DIRAS+),用于实时,频谱特定的 λ 预测.

主要成果:

  • 在SERS数据集上,DIRAS+在ALS和SEALS上表现优越.
  • 实现了更好的峰值保真度,减少了类内变化,并将基线扭曲最小化.
  • 迪拉斯提高了校准和化学测量模型的性能,提高了分析灵敏度.

结论:

  • 迪拉斯和迪拉斯+为拉曼光谱学提供了强大,可扩展和用户适应的解决方案.
  • 这些方法自动化了基线校正,这对于高通量光谱分析至关重要.
  • 迪拉斯+可实现实时,频谱特定的基线校正,推进光谱解释.