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相关概念视频

Spectrophotometry: Introduction01:16

Spectrophotometry: Introduction

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Spectrophotometry is the quantitative measurement of the absorption, reflection, diffraction, or transmission of electromagnetic radiation through a material as a function of the intensity and wavelength of the radiation. A spectrophotometer is a device used to measure the change in the radiation intensity caused by its interaction with the material.
The essential components of a spectrophotometer include a source of electromagnetic radiation, a slot for placing a material to be analyzed, and a...
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The absorbance of UV and visible (UV–visible) radiations is measured using a UV–visible spectrophotometer. Deuterium lamps, which emit UV radiation, and tungsten lamps, which produce radiation in the visible region, are used as light sources in UV–visible spectrophotometers. A monochromator or prism is used for diffraction grating, i.e., to split the incoming radiation into different wavelengths. A system of slits is used to focus the desired wavelength on the sample cell.
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Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
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设计一种使用错误强大的机器学习模型,用于分散反射频谱的定量分析.

Allison Scarbrough1, Keke Chen2, Bing Yu1

  • 1Marquette University and Medical College of Wisconsin, Joint Biomedical Engineering Department, Milwaukee, Wisconsin, United States.

Journal of biomedical optics
|January 12, 2024
PubMed
概括

一个新的波长独立回归器 (WIR) 模型使用分散反射光谱 (DRS) 准确预测组织光学特性. 这种机器学习方法对常见的使用错误具有稳定性,为传统模拟提供了更快,更可靠的替代方案.

关键词:
癌症检测 癌症检测扩散反射光谱学 扩散反射光谱学机器学习是机器学习.它们具有光学特性.

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科学领域:

  • 生物医学光学 生物医学光学
  • 医学物理 医学物理
  • 计算生物学 计算生物学

背景情况:

  • 扩散反射光谱 (DRS) 是用于非侵入性测量组织光学性质的宝贵工具.
  • 传统的方法,如逆蒙特卡洛 (MCI) 模拟,在计算上是密集的,对实验错误敏感.
  • 机器学习 (ML) 为从DRS数据中更快,更强大的光学属性预测提供了一个有希望的途径.

研究的目的:

  • 开发一种机器学习算法,用于从DRS光谱中预测组织光学特性,这种算法对常用错误具有强大耐用性.
  • 评估开发的算法的性能与已建立的模拟方法对比.

主要方法:

  • 开发了一个波长独立回归器 (WIR) 模型,从DRS数据中预测吸收系数 () 和减少散射系数 ().
  • 模拟的DRS光谱 (n=1520) 使用前进的蒙特卡洛模型生成,结合使用错误,如波长误校准和强度波动.
  • 从模仿组织的幽灵收集和分析了DRS实验数据 (n=882).

主要成果:

  • 与MCI模拟相比,WIR模型显示出更高的准确性和速度,特别是当与使用错误相结合时.
  • 在具有使用错误的模拟数据上,WIR实现了1.75%的数学错误和1.53%的数学错误的平均误差,显著超过MCI.
  • 对于实验数据,WIR模型的平均误差为13.2%和6.1%,MCI误差大约是MCI的八倍.

结论:

  • 该WIR模型提供可靠和使用错误强大的DRS数据光学属性的预测.
  • 这种基于机器学习的方法比DRS数据分析的传统模拟方法有了显著的进步.
  • WIR模型有潜力通过提供更快,更准确的组织特征来提高DRS的临床适用性.