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
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)
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
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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