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相关概念视频

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

320
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...
320
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

349
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...
349
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

819
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...
819

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相关实验视频

Updated: Jun 17, 2025

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems
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在拉曼光谱学中使用新型窗口功能进行增强的数据预处理:利用特征选择和机器学习来识别树来源.

Yaju Zhao1, Wei Lv1, Yinsheng Zhang1

  • 1Zhejiang Engineering Research Institute of Food & Drug Quality and Safety, Zhejiang Gongshang University, Hangzhou 310018, PR China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|August 10, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种使用拉曼光谱,特征选择和机器学习的新方法,以准确识别树起源. 这种方法提高了农产品的真实性和质量控制.

关键词:
选择功能选择功能选择.机器学习 机器学习原产地标识来源标识拉曼光谱法 拉曼光谱法窗口功能 窗口功能

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Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall
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科学领域:

  • 农业科学 农业科学
  • 分析化学 分析化学
  • 数据科学数据科学数据科学

背景情况:

  • 准确的原产地识别对于农产品的认证至关重要.
  • 拉曼光谱为化学分析提供了一种非破坏性的方法.
  • 传统的原产地识别方法可能是劳动密集型和不太准确的.

研究的目的:

  • 开发一个强大的,准确的方法来识别树的来源.
  • 探索结合新型光谱预处理,特征选择和机器学习算法的有效性.
  • 为农产品认证和质量控制提供可靠的工具.

主要方法:

  • 拉曼光谱数据预处理使用窗口函数与基线删除相结合.
  • 优化窗口功能的参数 (窗口宽度 = 5).
  • 应用特征选择技术,信息获取显示出卓越的性能.
  • 使用十种不同的机器学习算法构建和评估预测模型.

主要成果:

  • 优化的预处理大大降低了数据维度,提高了数据质量.
  • 信息获取有效地提取了歧视性光谱特征.
  • 线性支向量分类器 (LinearSVC),多层感知子分类器 (MLPClassifier) 和线性差异分析 (LDA) 实现了超过0.96.9的性能指标.
  • 随机向量功能链路网络分类器 (RVFLClassifier) 实现了超过0.93的性能指标.

结论:

  • 拟议的方法在识别树来源方面表现出高度准确性和稳定性.
  • 集成先进的光谱预处理,特征选择和机器学习是有效的.
  • 这种方法为农产品的认证和质量控制提供了有价值的工具.