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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.
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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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Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
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Updated: Sep 15, 2025

Rapid Antimicrobial Susceptibility Testing by Stimulated Raman Scattering Imaging of Deuterium Incorporation in a Single Bacterium
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Deep learning-assisted Raman spectroscopy for rapid lactic acid bacteria identification at the colony level.

Yu Wang1, Lei Xu2, Lindong Shang1

  • 1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China; State Key Laboratory of Applied Optics, Changchun 130033, PR China; Key Laboratory of Advanced Manufacturing for Optical Systems, Chinese Academy of Sciences, Changchun 130033, PR China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|July 15, 2025
PubMed
Summary

We developed an adaptive colony Raman acquisition method (ACRA-SNR) and a Raman Swin Transformer (Ra-ST) model for rapid bacterial identification. This combination achieves high accuracy in classifying and identifying lactic acid bacteria strains, enhancing industrial production efficiency.

Keywords:
Bacterial colonyIdentificationLactic acid bacteriaRaman spectroscopySwin transformer

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Area of Science:

  • Microbiology
  • Spectroscopy
  • Artificial Intelligence

Background:

  • Accurate and efficient identification of bacterial colonies is crucial for industrial applications.
  • Traditional methods face challenges with speed, accuracy, and spatial heterogeneity within colonies.
  • Novel approaches are needed to improve bacterial classification and selection processes.

Purpose of the Study:

  • To develop a rapid and accurate in situ method for bacterial colony identification.
  • To improve the efficiency of colony selection in industrial settings.
  • To evaluate the performance of a novel Raman spectroscopy acquisition technique combined with a deep learning model.

Main Methods:

  • An adaptive colony Raman acquisition method based on signal-to-noise ratio screening (ACRA-SNR) was proposed for in situ spectral acquisition.
  • The ACRA-SNR method was integrated with the Raman Swin Transformer (Ra-ST) model for bacterial classification.
  • Spectral data from fourteen lactic acid bacteria (LAB) strains were analyzed using the Ra-ST model.
  • Model generalization was assessed by identifying LAB strains from different sources.

Main Results:

  • The Ra-ST model achieved a high classification accuracy of 98.2% for fourteen LAB strains.
  • The model demonstrated good generalization capabilities with identification accuracy above 70% for external LAB strains.
  • Comparative analysis showed the Ra-ST model outperformed other models in both classification and prediction tasks.
  • The ACRA-SNR technique effectively mitigated the impact of spatial heterogeneity within colonies.

Conclusions:

  • The combination of ACRA-SNR and Ra-ST offers a powerful tool for rapid and accurate bacterial classification and identification.
  • This approach is expected to significantly enhance efficiency and output in industrial production of LAB and other functional bacteria.
  • The developed method shows promise for advancing microbial identification technologies in various sectors.