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

Area of Science:

• Analytical Chemistry
• Food Science
• Spectroscopy

Background:

• Veterinary drug residues in food pose significant food safety challenges.
• Surface-enhanced Raman scattering (SERS) offers high sensitivity and specificity for residue detection.
• Developing reliable SERS substrates and interpreting spectral data remain key hurdles.

Purpose of the Study:

• To review the development of SERS substrates for veterinary drug detection.
• To explore the integration of deep learning in SERS-based analysis.
• To identify limitations and propose future directions for enhanced food safety.

Main Methods:

• Categorization of SERS substrates (metal-based, rigid, flexible, multifunctional).
• Discussion of application scenarios and detection requirements.
• Integration of deep learning for substrate design, spectral analysis, and data interpretation.

Main Results:

• Overview of diverse SERS substrate types and their applications in veterinary drug detection.
• Demonstration of deep learning's potential in optimizing SERS performance and data analysis.
• Identification of challenges including reporter molecule selection and computational demands.

Conclusions:

• SERS, especially when combined with deep learning, shows great promise for sensitive and specific veterinary drug residue detection.
• Further research is needed to overcome limitations in substrate development and data processing for robust food safety applications.
• Future trends point towards multifunctional substrates and advanced computational methods for improved analytical capabilities.