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Related Concept Videos

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

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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...
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Raman Spectroscopy Instrumentation: Overview01:26

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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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Updated: Mar 19, 2026

Surface Enhanced Raman Spectroscopy Detection of Biomolecules Using EBL Fabricated Nanostructured Substrates
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DNN-enhanced spatially offset Raman spectroscopy for non-destructive subsurface material characterization.

Bo Wang, Pu Zhang, Xiangping Zhu

    Applied Optics
    |March 17, 2026
    PubMed
    Summary

    A deep neural network (DNN) enhances Spatially Offset Raman Spectroscopy (SORS) for non-destructive subsurface analysis. This automated method accurately reconstructs deep layer spectra, overcoming limitations of traditional SORS techniques.

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    Area of Science:

    • Analytical Chemistry
    • Spectroscopy
    • Computational Science

    Background:

    • Spatially Offset Raman Spectroscopy (SORS) offers non-destructive subsurface analysis.
    • Traditional SORS methods require manual parameter settings and lack automation.
    • Surface layer interference complicates subsurface SORS analysis.

    Purpose of the Study:

    • To develop a fully automated SORS method using deep neural networks (DNNs).
    • To simplify the spectrum reconstruction process in SORS.
    • To eliminate surface layer influence for accurate deeper layer spectral analysis.

    Main Methods:

    • A deep neural network (DNN) model was developed for SORS spectrum reconstruction.
    • The DNN model utilizes zero and non-zero offset inputs.
    • The method focuses on reconstructing pure Raman spectra of deeper sample layers.

    Main Results:

    • The DNN-enhanced SORS method achieved high reconstruction accuracy.
    • Root-mean-square error (RMSE) was below 0.02.
    • Pearson correlation coefficient (PCC) exceeded 0.96 for four tested powders.

    Conclusions:

    • The DNN-enhanced SORS method significantly simplifies spectrum reconstruction.
    • This approach enables accurate analysis of deeper sample layers.
    • The developed method shows strong potential for fully automated SORS data analysis.