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Related Experiment Video

Updated: Mar 8, 2026

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Leveraging Laser-Induced Breakdown Spectroscopy and Machine Learning Methods for Rapid Detection of AMR Profiles in

Vivek Sivakumar1, Sujatha N Unni1, Nilesh J Vasa2

  • 1Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology- Madras, Chennai, India.

Journal of Biophotonics
|March 7, 2026
PubMed
Summary

Rapid antimicrobial resistance (AMR) detection is crucial. A novel Laser-Induced Breakdown Spectroscopy (LIBS) method with machine learning accurately identifies bacterial resistance profiles, offering a faster alternative to traditional testing.

Keywords:
antimicrobial resistancecontrolled growth environmentlaser‐induced breakdown spectroscopymachine learningrapid diagnostics

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Area of Science:

  • Microbiology
  • Spectroscopy
  • Machine Learning

Background:

  • Antimicrobial resistance (AMR) poses a significant threat to public health, necessitating rapid detection methods.
  • Current culture-based susceptibility testing is time-consuming, delaying effective clinical treatment and infection control.

Purpose of the Study:

  • To develop and validate a Laser-Induced Breakdown Spectroscopy (LIBS) method for rapid antimicrobial susceptibility testing.
  • To accurately detect various resistance profiles in pathogenic bacteria using LIBS and machine learning.

Main Methods:

  • Utilized a controlled growth environment for bacterial culture to minimize spectral variability.
  • Employed Laser-Induced Breakdown Spectroscopy (LIBS) to analyze minimal yet robust spectral features (Sodium, Potassium, Calcium emission lines).
  • Applied machine learning algorithms, specifically a Support Vector Machine model, for classifying bacterial strains based on spectral data.

Main Results:

  • Achieved a classification accuracy of 94.7% for seven bacterial strains (3 susceptible, 4 resistant).
  • Obtained a Receiver Operating Characteristic Area Under the Curve (ROC-AUC) greater than 0.99.
  • Demonstrated accurate AMR detection without the need for outlier filtering or spectral averaging.

Conclusions:

  • The developed LIBS framework provides a rapid, cost-effective diagnostic tool for antimicrobial resistance profiling.
  • This method offers a significant improvement over conventional culture-based susceptibility testing in terms of speed and efficiency.
  • The LIBS approach holds promise for enhancing clinical treatment efficacy and infection control through timely AMR detection.