Emerging trends in SERS-based veterinary drug detection: multifunctional substrates and intelligent data approaches

Veterinary drug residues in poultry and livestock products present persistent challenges to food safety, necessitating precise and efficient detection methods. Surface-enhanced Raman scattering (SERS) has been identified as a powerful tool for veterinary drug residue analysis due to its high sensiti...

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Published inNPJ science of food Vol. 9; no. 1; pp. 31 - 21
Main Authors Yin, Tianzhen, Peng, Yankun, Chao, Kuanglin, Li, Yongyu
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 15.03.2025
Nature Publishing Group
Nature Portfolio
Subjects
692/499
706/1143
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Deep learning
Design optimization
Food Microbiology
Food safety
Food Science
Livestock
Nutrition
Optical properties
Qualitative analysis
Raman spectra
Residues
Review
Substrates
Trends
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Summary:Veterinary drug residues in poultry and livestock products present persistent challenges to food safety, necessitating precise and efficient detection methods. Surface-enhanced Raman scattering (SERS) has been identified as a powerful tool for veterinary drug residue analysis due to its high sensitivity and specificity. However, the development of reliable SERS substrates and the interpretation of complex spectral data remain significant obstacles. This review summarizes the development process of SERS substrates, categorizing them into metal-based, rigid, and flexible substrates, and highlighting the emerging trend of multifunctional substrates. The diverse application scenarios and detection requirements for these substrates are also discussed, with a focus on their use in veterinary drug detection. Furthermore, the integration of deep learning techniques into SERS-based detection is explored, including substrate structure design optimization, optical property prediction, spectral preprocessing, and both qualitative and quantitative spectral analyses. Finally, key limitations are briefly outlined, such as challenges in selecting reporter molecules, data imbalance, and computational demands. Future trends and directions for improving SERS-based veterinary drug detection are proposed.
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ISSN:2396-8370
2396-8370
DOI:10.1038/s41538-025-00393-z