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SERS-ATB: A comprehensive database server for antibiotic SERS spectral visualization and deep-learning

Quan Yuan1, Jia-Wei Tang2, Jie Chen3

  • 1School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China; Department of Laboratory Medicine, Shengli Oilfield Central Hospital, Dongying, Shandong Province, China.

Environmental Pollution (Barking, Essex : 1987)
|March 20, 2025
PubMed
Summary

A new database of 12,800 surface-enhanced Raman spectroscopy (SERS) spectra for 200 antibiotics was created to combat antibiotic pollution. A convolutional neural network (CNN) model achieved 98.94% accuracy for identification, aiding environmental monitoring.

Keywords:
AntibioticConvolutional neural networkMachine learning algorithmRaman spectraSurface-enhanced Raman spectroscopy

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Antibiotic pollution in water sources is a growing environmental and health concern.
  • Antibiotic resistance spread is exacerbated by environmental contamination.
  • Surface-enhanced Raman spectroscopy (SERS) offers sensitive antibiotic identification but lacks comprehensive spectral databases.

Purpose of the Study:

  • To develop a large-scale, open-access spectral database for antibiotics using SERS.
  • To establish a reliable machine learning model for antibiotic identification from SERS data.
  • To facilitate environmental monitoring and management of antibiotic pollution.

Main Methods:

  • Systematic collection of 12,800 SERS spectra for 200 environmentally relevant antibiotics.
  • Development of a web-based, open-access SERS spectral database.
  • Comparison and validation of machine learning algorithms, including a convolutional neural network (CNN), for spectral identification.

Main Results:

  • An open-access SERS spectral database for 200 antibiotics was established (http://sers.test.bniu.net/).
  • A CNN model achieved 98.94% accuracy in identifying antibiotics within the database.
  • External validation of the CNN model showed 82.8% accuracy, demonstrating practical utility.

Conclusions:

  • The SERS spectral database and CNN model provide a novel, scalable resource for environmental antibiotic detection.
  • This resource enhances the integration of SERS into environmental monitoring programs.
  • The study supports improved antibiotic pollution management and mitigation of antibiotic resistance.