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Raman Spectroscopy: Overview01:20

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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.
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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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在Raman光谱分类与机器学习的最新进展.

Yonghao Liu1, Yizhan Wu1, Junjie Wang2

  • 1College of New Energy, China University of Petroleum (East China), Qingdao 266580, China.

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概括
此摘要是机器生成的。

机器学习和深度学习通过自动分析复杂数据以进行准确的分类来显著提高拉曼光谱. 本综述探讨了在诊断和安全等领域的ML辅助拉曼光谱分析,确定了未来的研究方向.

关键词:
拉曼光谱法 拉曼光谱法深度学习是一种深度学习.机器学习是机器学习.根据光谱分类的分类.

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科学领域:

  • 分析化学 分析化学
  • 频谱学是一种光谱学.
  • 数据科学数据科学数据科学

背景情况:

  • 拉曼光谱提供非破坏性分子分析,但面临弱信号和复杂数据的挑战.
  • 传统的化学测量方法与非线性拉曼数据作斗争,需要手动的特征工程.

研究的目的:

  • 审查机器学习 (ML) 辅助拉曼光谱分类的研究进展,趋势和未来方向.
  • 在拉曼光谱分析中提供ML和深度学习 (DL) 应用的全面概述.

主要方法:

  • 结构化的叙事审查方法.
  • 对拉曼光谱数据的传统ML模型和先进DL架构的分析.
  • 确定关键的应用领域.

主要成果:

  • ML和DL可以从原始拉曼数据中实现自动特征学习,克服传统方法的局限性.
  • 在生物医学诊断,食品安全,矿物学和塑料识别方面,ML辅助拉曼光谱显示出前景.
  • 综述强调了ML/DL在拉曼光谱分类中的当前趋势和应用.

结论:

  • ML和DL融合为复杂的拉曼光谱解释和分类提供了强大的解决方案.
  • 尽管取得了进展,但有限的数据,概括性,可复制性和可解释性等挑战仍然存在.
  • 未来的研究应该解决这些挑战,以进一步推进ML辅助拉曼光谱.