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Related Concept Videos

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...
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...

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Related Experiment Video

Updated: May 15, 2026

Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy
09:57

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Published on: July 25, 2022

Dual-stream feature-decoupling network for rapid and robust rice traceability using handheld Raman spectroscopy.

Zhenfang Liu1, Jing Li1, Jungang Lou1

  • 1Zhejiang-French Digital Monitoring Lab for Aquatic Resources and Environment, Huzhou University, Huzhou, 313000, China.

Analytica Chimica Acta
|May 13, 2026
PubMed
Summary

A new Raman-Fluorescence Dual-stream Network (RFDNet) improves portable Raman spectroscopy for rice origin identification. This method enhances accuracy by separating Raman signals from fluorescence interference, boosting food safety and traceability.

Keywords:
Dual-stream neural networkNon-destructive detectionPortable spectroscopyRaman and fluorescenceRice traceability

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Rice classification and traceability are vital for food safety and quality management.
  • Handheld Raman spectroscopy offers rapid, non-destructive on-site analysis.
  • Portable Raman data often suffers from fluorescence interference, noise, and poor spectral utilization, limiting accuracy.

Purpose of the Study:

  • To develop a robust method for portable Raman-based rice origin identification.
  • To overcome limitations of fluorescence interference and spectral under-utilization in portable Raman instruments.

Main Methods:

  • Proposed the Raman-Fluorescence Dual-stream Network (RFDNet) framework.
  • Utilized wavelet decomposition and autoencoder for signal separation and refinement.
  • Employed a dual-branch network (Raman and fluorescence) with attention and morphological convolution for feature extraction.

Main Results:

  • RFDNet achieved 91% accuracy in rice origin identification.
  • Demonstrated a 2.25% improvement over established baseline models.
  • Effectively separated Raman signals from fluorescence and noise.

Conclusions:

  • The dual-stream approach offers a robust and generalizable solution for on-site rice traceability.
  • RFDNet effectively utilizes complementary information from Raman signals and fluorescence backgrounds.
  • Highlights potential for reliable authentication and quality management of agricultural products.