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

292
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...
292

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

Underwater Photogrammetry for the Study of Vulnerable Benthic Species: The Case of <i>Pinna rudis</i> Linnaeus, 1758.

Animals : an open access journal from MDPI·2026
Same author

Evaluation of Self-Illuminating Nanoconjugates Against Pancreatic Ductal Adenocarcinoma.

International journal of nanomedicine·2026
Same author

Capillary-based optical fiber sensor for turbidity measurement.

Scientific reports·2026
Same author

Genomic Characterization of a Rare K30-ST198 Hypervirulent <i>Klebsiella pneumoniae</i> Clone with Distinctive Virulence Features.

International journal of molecular sciences·2025
Same author

Smartphone-derived optical proxies for estimating toxicity risk of Microcystis aeruginosa complex in inland waters.

Environmental monitoring and assessment·2025
Same author

Plasmonic and Dielectric Metasurfaces for Enhanced Spectroscopic Techniques.

Biosensors·2025

相关实验视频

Updated: May 22, 2025

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

3.7K

综合拉曼光谱分析,通过多通道1D-CNN和基于SHAP的可解释性来区分有毒的蓝藻细菌.

María Gabriela Fernández-Manteca1, Borja García García1, Susana Deus Álvarez2

  • 1Photonics Engineering Group, Universidad de Cantabria, 39005, Santander, Spain; Instituto de Investigación Sanitaria Valdecilla (IDIVAL), 39011, Santander, Spain.

Talanta
|March 13, 2025
PubMed
概括

这项研究结合了拉曼光谱学和深度学习,以准确识别有毒蓝藻物种,改善有害藻类繁殖的检测. 多道深度学习方法实现了86%的准确性,提高了水质监测.

关键词:
菌检测检测 菌检测有害的藻类开花 有害的藻类开花一维卷积神经网络是一维卷积神经网络.拉曼光谱法 拉曼光谱法莎普利的附加式解释水质监测 监测水质 监测水质

更多相关视频

Experimental Protocol for Detecting Cyanobacteria in Liquid and Solid Samples with an Antibody Microarray Chip
10:57

Experimental Protocol for Detecting Cyanobacteria in Liquid and Solid Samples with an Antibody Microarray Chip

Published on: February 7, 2017

9.0K
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

9.4K

相关实验视频

Last Updated: May 22, 2025

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

3.7K
Experimental Protocol for Detecting Cyanobacteria in Liquid and Solid Samples with an Antibody Microarray Chip
10:57

Experimental Protocol for Detecting Cyanobacteria in Liquid and Solid Samples with an Antibody Microarray Chip

Published on: February 7, 2017

9.0K
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

9.4K

科学领域:

  • 环境科学 环境科学
  • 生物技术是生物技术.
  • 频谱学是一种光谱学.

背景情况:

  • 蓝藻细菌的繁殖对水质和公共卫生构成风险,原因是毒素的产生.
  • 准确识别蓝藻物种对于有效监测和管理有害藻类繁殖 (HAB) 是必不可少的.

研究的目的:

  • 开发和评估基于拉曼光谱的深度学习方法,用于分类四种有毒的蓝藻细菌物种.
  • 为了提高 HAB 监测中蓝藻细菌物种识别的准确性和可解释性.

主要方法:

  • 使用共聚焦拉曼显微镜 (532nm激发) 从四种有毒蓝藻细菌中获取拉曼光谱.
  • 多通道一维卷积神经网络 (1D-CNN) 的应用,包括原始,基线和预处理的光谱数据.
  • 使用沙普利增量解释 (SHAP) 进行光谱区域的解释性.

主要成果:

  • 多通道1D-CNN的分类准确率达到86%,超过单通道1D-CNN (74%).
  • 与传统方法相比,多道方法证明了过的减少.
  • SHAP分析确定了关键的光谱区域,对于准确的物种分类至关重要.

结论:

  • 将拉曼光谱与可解释的深度学习相结合,为水质监测提供了一个强大的工具.
  • 这种方法有助于早期检测和识别有害的藻类繁殖.
  • 开发的方法提高了蓝藻细菌物种分类的准确性和可解释性.