Surface-enhanced Raman scattering for the diagnosis of respiratory viruses

Surface-enhanced Raman scattering (SERS) has become a promising tool for rapid and sensitive respiratory pathogen detection. However, relevant reviews remain scarce. This review systematically summarizes the evolution of SERS technology in respiratory pathogen diagnosis from three key aspects: diagnostic target selection, spectral analysis strategy, and multimodal integration. The detection paradigm has advanced from single-target identification to multiple-target detection for co-infection and mutation resistance, and further to cross-species detection focusing on conserved sequences of zoonotic pathogens. Meanwhile, spectral analysis has shifted from single-peak judgment to full-spectrum intelligence combined with artificial intelligence. Artificial intelligence-assisted spectral analysis has significantly improved the accuracy and robustness of full-spectrum analysis. In addition, traditional single-modal SERS is evolving into multimodal integrated platforms to break through the limitations of single-modal sensing and detecting complex samples. Finally, this review discusses the challenges and future directions for SERS diagnostic platforms in terms of intelligence and portability.