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

Synthesis and Decomposition Reactions02:17

Synthesis and Decomposition Reactions

38.1K
Synthesis and decomposition are two types of redox reactions. Synthesis means to make something, whereas decomposition means to break something. The reactions are accompanied by chemical and energy changes. 
38.1K
X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

4.7K
X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
4.7K
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

8.9K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
8.9K
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

9.4K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
9.4K
Introduction to Biological Bases of Psychology01:30

Introduction to Biological Bases of Psychology

4.5K
Biopsychology serves as a vital bridge connecting the intricate domains of biology and psychology, shedding light on how biological systems influence psychological phenomena. This field scrutinizes the biological substrates of behavior and mental processes, emphasizing the nervous system along with the roles of neurotransmitters, hormones, and genetics. It also incorporates evolutionary perspectives to explain the adaptive nature of mental functions.
The nervous system, the cornerstone of...
4.5K
What is a Mode?01:07

What is a Mode?

25.1K
The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
25.1K

You might also read

Related Articles

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

Sort by
Same author

A-phase Occurrence During Sleep in the Deep Brain Recordings: Multiscale-Entropy and Multiscale-DFA Analysis.

Brain topography·2026
Same author

Robust end-to-end stratification of amyotrophic lateral sclerosis patients via recurrent variational autoencoder and consensus clustering.

Journal of biomedical informatics·2026
Same author

Assessment of Multifractal-Multiscale Properties of Heart Rate Variability in Sleep Disorders.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Assessing Smooth Pursuit Eye Movements Using Eye-Tracking Technology in Patients with Schizophrenia Under Treatment: A Pilot Study.

Sensors (Basel, Switzerland)·2025
Same author

NAFLD progression in metabolic syndrome: a Raman spectroscopy and machine learning approach in an animal model.

The Analyst·2025
Same author

Editorial: Stress and the brain: advances in neurophysiological measures for mental stress detection and reduction.

Frontiers in neuroergonomics·2024

Related Experiment Video

Updated: Jan 20, 2026

A Novel Technique for Raman Analysis of Highly Radioactive Samples Using Any Standard Micro-Raman Spectrometer
07:52

A Novel Technique for Raman Analysis of Highly Radioactive Samples Using Any Standard Micro-Raman Spectrometer

Published on: April 12, 2017

13.3K

Improved Vancouver Raman Algorithm Based on Empirical Mode Decomposition for Denoising Biological Samples.

Fabiola León-Bejarano1, Martin O Méndez2, Miguel G Ramírez-Elías1

  • 1Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, SLP, México.

Applied Spectroscopy
|August 15, 2019
PubMed
Summary

A new modified Vancouver Raman algorithm (mVRA) uses empirical mode decomposition (EMD) to improve Raman spectral denoising. This method enhances signal quality and automates baseline correction for biological samples, preserving crucial low-amplitude peaks.

Keywords:
Background removalRaman spectroscopyautofluorescencepolynomial fittingsignal denoisingtime frequency analysis

More Related Videos

Stimulated Stokes and Antistokes Raman Scattering in Microspherical Whispering Gallery Mode Resonators
12:21

Stimulated Stokes and Antistokes Raman Scattering in Microspherical Whispering Gallery Mode Resonators

Published on: April 4, 2016

11.7K
NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode
09:19

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

Published on: June 4, 2021

3.8K

Related Experiment Videos

Last Updated: Jan 20, 2026

A Novel Technique for Raman Analysis of Highly Radioactive Samples Using Any Standard Micro-Raman Spectrometer
07:52

A Novel Technique for Raman Analysis of Highly Radioactive Samples Using Any Standard Micro-Raman Spectrometer

Published on: April 12, 2017

13.3K
Stimulated Stokes and Antistokes Raman Scattering in Microspherical Whispering Gallery Mode Resonators
12:21

Stimulated Stokes and Antistokes Raman Scattering in Microspherical Whispering Gallery Mode Resonators

Published on: April 4, 2016

11.7K
NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode
09:19

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

Published on: June 4, 2021

3.8K

Area of Science:

  • Spectroscopy
  • Biophysics
  • Signal Processing

Background:

  • Raman spectroscopy is vital for analyzing biological samples.
  • Traditional Vancouver Raman algorithm (VRA) denoising has limitations, including subjectivity and potential loss of low-intensity spectral peaks.
  • Existing methods struggle with accurate baseline correction in complex biological spectra.

Purpose of the Study:

  • To introduce a novel modified Vancouver Raman algorithm (mVRA) for enhanced denoising of Raman spectra.
  • To improve upon the limitations of the standard VRA by incorporating empirical mode decomposition (EMD).
  • To automate parameter selection and baseline correction in Raman spectral analysis.

Main Methods:

  • Developed mVRA by replacing the VRA's mean filter with EMD for adaptive, parameter-free signal filtering.
  • Automated the selection of polynomial degree for fitting spectral data.
  • Compared mVRA against VRA and EMD using artificial, synthetic, and biological (human nail, mouse brain) Raman spectra.
  • Utilized the correlation coefficient (ρ) to evaluate denoising performance and baseline modeling.

Main Results:

  • mVRA demonstrated superior denoising compared to VRA and EMD, especially at moderate to high noise levels for artificial spectra.
  • mVRA achieved consistent results in modeling the underlying fluorescence signal (baseline trend).
  • For biological samples, mVRA preserved valuable low-amplitude Raman peaks, outperforming EMD and showing comparable results to VRA.

Conclusions:

  • The integration of EMD into VRA (mVRA) offers a robust alternative for denoising biological Raman spectra.
  • mVRA effectively conserves essential spectral information, particularly low-amplitude peaks, crucial for accurate analysis.
  • The mVRA method automates baseline correction and eliminates the need for subjective parameter tuning, enhancing reproducibility.