Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

585
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...
585
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

736
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...
736
Gas Chromatography: Overview of Detectors01:13

Gas Chromatography: Overview of Detectors

947
Detectors in gas chromatography (GC) help identify and quantify the components of a mixture by translating chemical properties into measurable signals, which are displayed on a chromatogram. Detectors can be categorized into two main types: destructive and non-destructive.
A non-destructive detector allows a sample to be analyzed without altering or consuming it, meaning the sample can be collected after detection for further analysis. Examples include thermal conductivity detectors and...
947
Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

587
In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...
587
NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

802
When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
802
NMR Spectroscopy: Chemical Shift Overview01:15

NMR Spectroscopy: Chemical Shift Overview

1.8K
The position of the absorption signal of a sample is reported relative to the position of the signal of tetramethylsilane (TMS), which is added as an internal reference while recording spectra. The difference between the absorption frequencies of the sample and TMS (in Hz) is divided by the spectrometer operating frequency (in MHz) to obtain a dimensionless quantity called the chemical shift. It is reported on the δ (delta) scale and expressed in parts per million.
For instance, the proton...
1.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

HSG-ON: Hierarchical Scene Graph-Based Object Navigation.

Sensors (Basel, Switzerland)·2026
Same author

Plasma p-tau217 and Aβ42/40 as markers of Aβ pathology in the Lewy body continuum.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Cholinergic basal forebrain degeneration in isolated REM sleep behaviour disorder.

Brain : a journal of neurology·2025
Same author

Cholinergic degeneration and early cognitive signs in prodromal Lewy body dementia.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Mild behavioral impairment and its relation to amyloid load in isolated REM sleep behavior disorder.

Parkinsonism & related disorders·2025
Same author

Cognitive Impact of β-Amyloid Load in the Rapid Eye Movement Sleep Behavior Disorder-Lewy Body Disease Continuum.

Movement disorders : official journal of the Movement Disorder Society·2024

Related Experiment Video

Updated: Oct 8, 2025

Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy
09:57

Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

Published on: July 25, 2022

4.1K

An Effective Baseline Correction Algorithm Using Broad Gaussian Vectors for Chemical Agent Detection with Known Raman

Hyeong Geun Yu1, Dong Jo Park1, Dong Eui Chang1

  • 1School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea.

Sensors (Basel, Switzerland)
|December 28, 2021
PubMed
Summary

This study introduces a novel baseline correction algorithm for Raman spectroscopy, significantly improving chemical agent detection. The method effectively removes fluorescence-induced baselines while preserving crucial spectral data.

Keywords:
Raman spectroscopybaseline correctionchemical agent detectiongeneralized likelihood ratio testsignal processing

More Related Videos

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems
09:57

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems

Published on: February 10, 2020

7.3K
Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
10:14

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Published on: September 2, 2020

5.1K

Related Experiment Videos

Last Updated: Oct 8, 2025

Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy
09:57

Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

Published on: July 25, 2022

4.1K
Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems
09:57

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems

Published on: February 10, 2020

7.3K
Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
10:14

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Published on: September 2, 2020

5.1K

Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemical Sensing

Background:

  • Raman spectroscopy is vital for non-contact chemical agent (CA) detection.
  • Fluorescence-induced baseline in Raman spectra degrades CA detection algorithm performance.

Purpose of the Study:

  • To develop a novel baseline correction algorithm for Raman spectroscopy.
  • To minimize spectral distortion while removing fluorescence baselines.
  • To enhance chemical agent detection performance.

Main Methods:

  • Modeling the measured spectrum as a linear combination of Gaussian vectors, background bases, and CA reference spectra.
  • Estimating the baseline and Raman spectrum simultaneously using the least squares method.
  • Discussing design parameters for Gaussian vectors.

Main Results:

  • The proposed algorithm effectively removes baselines caused by fluorescence.
  • It minimizes distortion of the original Raman scattering spectrum.
  • Experimental results show improved CA detection performance compared to conventional methods.

Conclusions:

  • The developed algorithm offers superior baseline correction for Raman spectroscopy.
  • It enhances the accuracy and reliability of chemical agent detection.
  • The method requires prior knowledge of CA reference spectra and background matrices.