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相关概念视频

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

537
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...
537
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

618
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...
618

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相关实验视频

Updated: Sep 16, 2025

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects
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在高通量实验中进行自动拉曼测量的设置.

Christoph Lange1, Simon Seidel1, Madeline Altmann1

  • 1Chair of Bioprocess Engineering, Technische Universität Berlin, Berlin, Germany.

Biotechnology and bioengineering
|July 11, 2025
PubMed
概括

这项研究引入了一种用于快速拉曼光谱测量的自动化系统,用于高通量生物技术,使得在细菌培养过程中更快地分析代谢物度.

关键词:
拉曼光谱学 拉曼光谱学 拉曼光谱学自动化自动化自动化自动化卷积神经网络是一种卷积神经网络.高通量生物处理的高通量生物处理

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Author Spotlight: Single-Molecule Surface-Enhanced Raman Scattering Measurements Enabled by Plasmonic DNA Origami Nanoantennas
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科学领域:

  • 生物技术和分析化学
  • 工艺工程和自动化过程工程.
  • 机器学习在科学研究中的应用

背景情况:

  • 高通量 (HT) 实验加速了生物研究,但通常受到慢速分析方法的限制.
  • 当前的分析技术很难跟上HT工作流中的快速样本生成.
  • 需要自动化,高速的分析解决方案来充分利用HT的能力.

研究的目的:

  • 开发和验证用于自动化,高通量拉曼光谱测量的综合系统.
  • 为了加快分析生物样本中的代谢物度.
  • 为了使大数据集的产生机器学习模型培训在生物技术.

主要方法:

  • 开发一个集物理设备和软件用于拉曼光谱的自动化系统.
  • 同时处理和测量八个并行的50μL样品.
  • 实施机器学习模型,从拉曼光谱中预测代谢物度 (葡萄糖和酸盐).

主要成果:

  • 该系统在45秒内完成了每个样本的测量,处理,清洁和度预测.
  • 机器学习模型实现的平均绝对误差为0.27g L-1对于葡萄糖和0.06g L-1对于乙酸盐.
  • 证明了用于发酵监测的一致的高通量光谱数据收集.

结论:

  • 自动化的拉曼光谱测量系统显著加快了生物技术中的分析吞吐量.
  • 综合机器学习方法可以准确预测代谢物度.
  • 这项技术支持生成广泛的数据集,用于开发强大的机器学习模型,用于生物过程分析.