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相关概念视频

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
682
Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
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IR Spectrometers01:25

IR Spectrometers

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There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
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相关实验视频

Updated: May 21, 2025

High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology
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弥合光谱差距:在基于血液的红外光谱学中跨设备模型概括.

Flora B Nemeth1,2,3, Niklas Leopold-Kerschbaumer2,3, Diana Debreceni1

  • 1Center for Molecular Fingerprinting (CMF), 1093 Budapest, Hungary.

Analytical chemistry
|May 7, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种数据增强方法,以改进机器学习模型对不同设备的血液红外光谱的概括. 该技术在福利埃变换红外光谱仪器 (FTIR) 之间传输模型时提高了模型的准确性和可靠性.

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Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis
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相关实验视频

Last Updated: May 21, 2025

High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology
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High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

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科学领域:

  • 频谱学是一种光谱学.
  • 机器学习 机器学习
  • 生物医学工程 生物医学工程

背景情况:

  • 血液红外光谱正在获得分析的普及.
  • 机器学习模型通常因设备变异而难以跨设备概括.
  • 确保不同工具的模型性能对于广泛采用至关重要.

研究的目的:

  • 开发一种改进血液红外光谱学跨设备模型概括的方法.
  • 提高机器学习模型对独特设备特征的适应性.
  • 为了验证一个新的领域适应技术.

主要方法:

  • 一种使用数据增强的简单域调整方法.
  • 将特定于设备的差异纳入增强训练数据.
  • 在两个不同的富里埃变换红外光谱仪器 (FTIR) 上进行实验验证.

主要成果:

  • 拟议的数据增强方法显著提高了预测准确度.
  • 当应用于不同的FTIR设备时,增强了模型可靠性.
  • 证明有效地适应设备间的变化.

结论:

  • 将设备特定差异纳入数据增强是血液红外光谱学中跨设备概括的有效策略.
  • 该方法增强了机器学习模型在这个领域的实际实用性.
  • 这种方法为在不同的实验室环境中部署光谱模型提供了可行的解决方案.