Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

416
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...
416
Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

431
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...
431
Response Surface Methodology01:16

Response Surface Methodology

146
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
146

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Inexpensive Hydrogen Storage: Propylene to Propane using Plasmonic Photocatalysis.

Nano letters·2026
Same author

Deep-Learning Inversion Maps Arbitrary Design Images to Low-Cost, Efficient Nanofabrication.

ACS nano·2026
Same author

Understanding SERS Spectral Shape Variability through Substrate Optics, Molecular Orientation, and Unsupervised Clustering.

The journal of physical chemistry. C, Nanomaterials and interfaces·2026
Same author

Comprehensive Open-Source Ecosystem for Raman and SERS Spectroscopy: Introducing SpectraGuru.

Analytical chemistry·2026
Same author

Quantitative Spectroscopic Characterization of Fuel Properties of Carbon-Based Materials Using Machine Learning.

Analytical chemistry·2026
Same author

SERS-Based E-Tongue Data Analysis Methods: From Spectrum Preparation to Qualitative and Quantitative Modeling.

ACS sensors·2026

相关实验视频

Updated: Jul 12, 2025

Surface Enhanced Raman Spectroscopy Detection of Biomolecules Using EBL Fabricated Nanostructured Substrates
11:44

Surface Enhanced Raman Spectroscopy Detection of Biomolecules Using EBL Fabricated Nanostructured Substrates

Published on: March 20, 2015

20.4K

使用机器学习与拉曼图书馆识别表面增强的拉曼光谱.

Yilong Ju, Oara Neumann, Mary Bajomo

  • 1Department of Physics and Astronomy, University of Georgia, Athens, Georgia 30602, United States.

ACS nano
|November 1, 2023
PubMed
概括

一个新的机器学习算法,特征峰值相似性 (CaPSim),使用表面增强拉曼光谱 (SERS) 数据准确识别化学物质. 这种方法克服了基质变异性,可用于可靠的SERS分析.

关键词:
具有特征的峰值相似性.机器学习是机器学习.纳米颗粒是一种纳米粒子.聚环芳香碳化合物 聚环芳香碳化合物表面增强的拉曼散射作用

更多相关视频

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
15:04

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

13.1K
An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects
07:37

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Published on: January 9, 2020

9.5K

相关实验视频

Last Updated: Jul 12, 2025

Surface Enhanced Raman Spectroscopy Detection of Biomolecules Using EBL Fabricated Nanostructured Substrates
11:44

Surface Enhanced Raman Spectroscopy Detection of Biomolecules Using EBL Fabricated Nanostructured Substrates

Published on: March 20, 2015

20.4K
Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
15:04

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

13.1K
An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects
07:37

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Published on: January 9, 2020

9.5K

科学领域:

  • 频谱学是一种光谱学.
  • 分析化学 分析化学
  • 机器学习 机器学习

背景情况:

  • 表面增强拉曼光谱 (SERS) 提供了快速,便携式的微量分子识别.
  • 在SERS基底的变化导致不一致的光谱数据,阻碍实际应用.
  • 现有的方法需要基板特定的光谱库,限制了广泛的可用性.

研究的目的:

  • 开发一种机器学习 (ML) 算法,用于使用SERS光谱进行化学识别.
  • 解决和克服SERS数据中基质特定变化的挑战.
  • 提高SERS对可实地应用的准确性和实用性.

主要方法:

  • 开发了一种使用特征提取的机器学习算法,类似于面部识别.
  • 引入了一种新的度量,特征峰值相似性 (CaPSim),专注于关键的光谱峰值.
  • 设计的CaPSim能够容纳和量化SERS光谱中的基质特异性变异性.

主要成果:

  • 与现有的算法相比,CaPSim指标在光谱匹配方面表现出卓越的准确性.
  • 机器学习方法成功地将SERS光谱与标准的拉曼光谱库相匹配.
  • CaPSim有效地处理SERS测量中固有的麻烦变量.

结论:

  • 开发的ML算法和CaPSim指标显著提高了基于SERS的化学品识别的准确性.
  • 这种方法减轻了对基板特定光谱库的需求.
  • 基于ML的SERS分析可在便携式,可现场设置中提供可靠的分子识别.