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Related Concept Videos

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

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 the...
Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

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...

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Related Experiment Video

Updated: Jun 9, 2026

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

Deep Learning-Based Restoration of Distorted Transmission Raman Spectra through Biological Tissue.

Haoqiang Xie1, Zhou Chen1,2, Yutong Zhou1

  • 1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China.

Analytical Chemistry
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning framework restores Raman spectra degraded by biological tissues. This method enhances signal intensity, reduces noise, and improves molecular quantification accuracy for in vivo applications.

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Agarose-based Tissue Mimicking Optical Phantoms for Diffuse Reflectance Spectroscopy
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Published on: August 22, 2018

Related Experiment Videos

Last Updated: Jun 9, 2026

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

Agarose-based Tissue Mimicking Optical Phantoms for Diffuse Reflectance Spectroscopy
09:25

Agarose-based Tissue Mimicking Optical Phantoms for Diffuse Reflectance Spectroscopy

Published on: August 22, 2018

Area of Science:

  • Spectroscopy
  • Biophotonics
  • Machine Learning

Background:

  • Raman spectroscopy provides chemical specificity for molecular analysis.
  • Deep Raman techniques like transmission Raman spectroscopy (TRS) allow subsurface probing.
  • Tissue scattering severely limits in vivo Raman spectroscopy accuracy due to signal attenuation and spectral distortion.

Purpose of the Study:

  • To develop a deep learning framework for restoring Raman spectra distorted by biological tissues.
  • To improve the quantitative accuracy of in vivo Raman spectroscopy.

Main Methods:

  • A dataset of 4410 paired pre- and post-transmission Raman/SERS spectra was created.
  • Tissue-induced spectral distortions were systematically characterized.
  • A 1D U-Net model was trained to learn the inverse transformation for spectral restoration.

Main Results:

  • The model effectively restored attenuated intensities, suppressed noise, and reconstructed spectral profiles.
  • Restored spectra showed >95% average cosine similarity to ground-truth profiles on an independent test set.
  • Molecular quantification accuracy was enhanced, with clearer concentration-response relationships for mixed SERS nanoparticles.

Conclusions:

  • Data-driven full-spectrum restoration effectively counteracts tissue-induced spectral degradation.
  • The developed framework significantly improves the accuracy of Raman-based quantification in scattering biological media.
  • This approach holds promise for advancing in vivo Raman spectroscopy applications.