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

Author Identifier

Liang Wang: https://orcid.org/0000-0001-5339-7484

Document Type

Journal Article

Publication Title

Environmental Pollution

Volume

373

Publisher

Elsevier

School

Centre for Precision Health / School of Medical and Health Sciences

Publication Unique Identifier

10.1016/j.envpol.2025.126083

Funders

National Natural Science Foundation of China (82372258) / Guangdong Basic and Applied Basic Research Foundation (2022A1515220023) / Research Foundation for Advanced Talents of Guandong Provincial People's Hospital (KY012023293) / National High-End Foreign Expert Project (H20240714)

Comments

Yuan, Q., Tang, J. W., Chen, J., Liao, Y. W., Zhang, W. W., Wen, X. R., ... & Wang, L. (2025). SERS-ATB: A comprehensive database server for antibiotic SERS spectral visualization and deep-learning identification. Environmental Pollution, 373, 126083. https://doi.org/10.1016/j.envpol.2025.126083

Abstract

The rapid and accurate identification of antibiotics in environmental samples is critical for addressing the growing concern of antibiotic pollution, particularly in water sources. Antibiotic contamination poses a significant risk to ecosystems and human health by contributing to the spread of antibiotic resistance. Surface-enhanced Raman spectroscopy (SERS), known for its high sensitivity and specificity, is a powerful tool for antibiotic identification. However, its broader application is constrained by the lack of a large-scale antibiotic spectral database crucial for environmental and clinical use. To address this need, we systematically collected 12,800 SERS spectra for 200 environmentally relevant antibiotics and developed an open-access, web-based database at http://sers.test.bniu.net/. We compared six machine learning algorithms with a convolutional neural network (CNN) model, which achieved the highest accuracy at 98.94%, making it the preferred database model. For external validation, CNN demonstrated an accuracy of 82.8%, underscoring its reliability and practicality for real-world applications. The SERS database and CNN prediction model represent a novel resource for environmental monitoring, offering significant advantages in terms of accessibility, speed, and scalability. This study establishes the large-scale, public SERS spectral databases for antibiotics, facilitating the integration of SERS into environmental programs, with the potential to improve antibiotic detection, pollution management, and resistance mitigation.

DOI

10.1016/j.envpol.2025.126083

Access Rights

subscription content

Link to Full Text

Share

 
COinS