Related Experiment Video
Updated: Jul 10, 2026
06:34
Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS
Published on: July 11, 2016
Spectroscopic discrimination of bacterial species of variable pathogenicity through explainable machine learning
Tanisha Singh1, Sunil Kumar Khare1,2, Soumik Siddhanta1
1Department of Chemistry, Indian Institute of Technology Delhi, New Delhi, 110016, India. soumik@chemistry.iitd.ac.in.
Nanoscale
|July 9, 2026
Summary
This study introduces a fast, label-free method using Surface-Enhanced Raman Spectroscopy (SERS) and artificial intelligence (AI) for bacterial identification. The AI model accurately identifies pathogens and their resistance, improving antimicrobial therapy decisions.
Area of Science:
- Biophotonics
- Spectroscopy
- Computational Biology
Background:
- Current bacterial identification methods are slow (24-48h), delaying treatment and promoting antimicrobial resistance (AMR).
- Surface-Enhanced Raman Spectroscopy (SERS) offers a rapid, label-free approach to detect bacterial biochemical fingerprints.
- Analyzing complex SERS data requires advanced computational tools for accurate pathogen identification.
Purpose of the Study:
- To develop and validate an explainable AI (XAI) workflow for rapid bacterial pathogen identification using SERS.
- To discriminate between five clinically relevant bacterial pathogens and assess their pathogenicity.
- To achieve transparent and reliable species-level identification and antimicrobial resistance phenotype detection.
Main Methods:
- Utilized Surface-Enhanced Raman Spectroscopy (SERS) to capture bacterial biochemical fingerprints.
- Employed supervised machine learning models including Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Random Forest (RF), and 1D Convolutional Neural Network (CNN).
- Applied SHapley Additive exPlanations (SHAP) for model interpretability and MCR-ALS for spectral decomposition into biochemical components.
Main Results:
- The 1D Convolutional Neural Network (CNN) achieved near-perfect classification accuracy for bacterial species identification.
- Achieved 100% discrimination between methicillin-resistant S. aureus (MRSA) and methicillin-sensitive S. aureus.
- SHAP analysis and MCR-ALS decomposition identified key Raman spectral regions and biochemical markers for pathogen differentiation, ensuring model transparency.
Conclusions:
- An end-to-end explainable workflow coupling SERS with interpretable AI provides a rapid and transparent method for pathogenic disease diagnosis.
- This approach can significantly improve the speed and accuracy of antimicrobial therapy guidance.
- Demonstrated the potential of AI-driven SERS for clinical applications in infectious disease management.
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