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: Overview01:20

Raman Spectroscopy: Overview

1.8K
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...
1.8K
Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

1.4K
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...
1.4K
NMR Spectroscopy: Chemical Shift Overview01:15

NMR Spectroscopy: Chemical Shift Overview

3.3K
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...
3.3K
Electric Potential and Potential Difference01:16

Electric Potential and Potential Difference

5.7K
Suppose a positive test charge moves away from a positive static charge, then the Coulomb force does positive work, and its electric potential energy decreases. The potential energy per unit charge is defined as the electric potential. The electric potential is independent of the test charge.
When a test charge moves from the initial to the final position, the electric potential difference between those positions is defined as the ratio of the change in the potential energy to the charge on the...
5.7K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.4K
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

3.8K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
3.8K

You might also read

Related Articles

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

Sort by
Same author

FOXM1 predicts disease progression in non-muscle invasive bladder cancer.

Journal of cancer research and clinical oncology·2018
Same author

TILGen: A Program to Investigate Immune Targets in Breast Cancer Patients - First Results on the Influence of Tumor-Infiltrating Lymphocytes.

Breast care (Basel, Switzerland)·2018
Same author

The effect of participation in neoadjuvant clinical trials on outcomes in patients with early breast cancer.

Breast cancer research and treatment·2018
Same author

A case of multiple familial trichoepitheliomas responding to treatment with the Hedgehog signaling pathway inhibitor vismodegib.

Virchows Archiv : an international journal of pathology·2018
Same author

Can clinicopathological parameters predict for lymph node metastases in ypT0-2 rectal carcinoma? Results of the CAO/ARO/AIO-94 and CAO/ARO/AIO-04 phase 3 trials.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology·2018
Same author

Subclassification, survival prediction and drug target analyses of chemotherapy-naïve muscle-invasive bladder cancer with a molecular screening.

Oncotarget·2018

Related Experiment Video

Updated: Feb 8, 2026

Resonance Raman Spectroscopy of Extreme Nanowires and Other 1D Systems
07:44

Resonance Raman Spectroscopy of Extreme Nanowires and Other 1D Systems

Published on: April 28, 2016

15.6K

Breast Tumor Analysis Using Shifted-Excitation Raman Difference Spectroscopy (SERDS).

Medhanie Tesfay Gebrekidan1,2,3, Ramona Erber4, Arndt Hartmann4

  • 11 Lehrstuhl für Technische Thermodynamik, Friedrich-Alexander-Universität (FAU), Erlangen-Nürnberg, Germany.

Technology in Cancer Research & Treatment
|July 12, 2018
PubMed
Summary

Shifted-excitation Raman difference spectroscopy effectively classifies breast tissue as normal, fibroadenoma, or invasive carcinoma. This Raman spectroscopy method aids in distinguishing between healthy and cancerous tissues, supporting pathology diagnoses.

Keywords:
Raman spectrabreast cancerfibroadenomafluorescence rejectionshifted-excitation Raman difference spectroscopy

More Related Videos

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy
13:48

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Published on: May 29, 2012

17.6K
Fabricating a UV-Vis and Raman Spectroscopy Immunoassay Platform
09:02

Fabricating a UV-Vis and Raman Spectroscopy Immunoassay Platform

Published on: November 10, 2016

10.8K

Related Experiment Videos

Last Updated: Feb 8, 2026

Resonance Raman Spectroscopy of Extreme Nanowires and Other 1D Systems
07:44

Resonance Raman Spectroscopy of Extreme Nanowires and Other 1D Systems

Published on: April 28, 2016

15.6K
Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy
13:48

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Published on: May 29, 2012

17.6K
Fabricating a UV-Vis and Raman Spectroscopy Immunoassay Platform
09:02

Fabricating a UV-Vis and Raman Spectroscopy Immunoassay Platform

Published on: November 10, 2016

10.8K

Area of Science:

  • Biomedical Optics
  • Spectroscopy
  • Medical Diagnostics

Background:

  • Accurate breast tissue classification is crucial for diagnosis.
  • Conventional histopathology can be time-consuming.
  • Raman spectroscopy offers label-free chemical information.

Purpose of the Study:

  • To evaluate shifted-excitation Raman difference spectroscopy (SERDS) for ex vivo breast tissue classification.
  • To differentiate between normal, fibroadenoma, and invasive carcinoma tissues.
  • To assess SERDS performance against histopathology.

Main Methods:

  • Utilized SERDS with 784/785 nm excitation on resected, formalin-fixed breast tissues.
  • Analyzed 8 invasive carcinoma and 3 fibroadenoma samples (240 measurements each).
  • Applied principal component analysis and linear discriminant analysis for spectral classification.

Main Results:

  • SERDS successfully differentiated normal from tumor tissues.
  • Pure Raman spectra isolated via SERDS enabled fibroadenoma/carcinoma distinction.
  • Invasive carcinoma identified with 99.15% sensitivity; absence with 90.40% specificity.
  • Tumor tissue detection achieved 100% sensitivity and specificity.

Conclusions:

  • SERDS is a promising technique for ex vivo breast tissue analysis.
  • The method provides high sensitivity and specificity in classifying breast pathologies.
  • SERDS has the potential to augment histopathological diagnosis in breast pathology.