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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: May 29, 2026

Label-Free Surface-Enhanced Raman Scattering Bioanalysis Based on Au@Carbon Dot Nanoprobes
06:19

Label-Free Surface-Enhanced Raman Scattering Bioanalysis Based on Au@Carbon Dot Nanoprobes

Published on: June 9, 2023

Current trends in machine learning for surface-enhanced Raman spectroscopy.

Ruihao Luo1,2, Sujia Jiao1, Jyothi B Nair1

  • 1Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany. dana.cialla-may@leibniz-ipht.de.

The Analyst
|May 28, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence is revolutionizing surface-enhanced Raman spectroscopy (SERS) analysis, making it more automated and scalable. Challenges like data scarcity and model explainability remain, requiring community efforts for FAIR data and interpretable AI.

Related Experiment Videos

Last Updated: May 29, 2026

Label-Free Surface-Enhanced Raman Scattering Bioanalysis Based on Au@Carbon Dot Nanoprobes
06:19

Label-Free Surface-Enhanced Raman Scattering Bioanalysis Based on Au@Carbon Dot Nanoprobes

Published on: June 9, 2023

Area of Science:

  • Surface-enhanced Raman spectroscopy (SERS)
  • Artificial Intelligence (AI) in Spectroscopy

Background:

  • SERS analysis is increasingly integrated with AI across its methodological spectrum.
  • Conventional machine learning and advanced deep learning architectures are key to SERS advancements.

Purpose of the Study:

  • To review the current AI methodologies transforming SERS analysis.
  • To outline practical guidelines and a future path for AI in SERS.

Main Methods:

  • Application of conventional machine learning for baseline correction and deployment.
  • Utilization of deep learning (CNNs, RNNs, Transformers) for spectral representation learning.
  • Employing generative models (GANs, VAEs, Diffusion) for data augmentation and denoising.
  • Leveraging large language models for metadata curation and decision support.

Main Results:

  • AI enhances SERS convenience, scalability, and automation for applications in medicine, agriculture, food, environment, and process control.
  • Significant progress in spectral representation learning and data augmentation through AI.
  • AI facilitates metadata curation and retrieval-augmented decision support in SERS.

Conclusions:

  • Despite AI advancements, challenges in data scarcity and model explainability persist.
  • Future directions emphasize FAIR data principles, transparent evaluation, and interpretable, uncertainty-aware AI models.
  • Community-driven efforts are crucial for robust and reliable AI-powered SERS deployment across diverse settings.