Abstract：Rapid and accurate detection of common viruses causing acute respiratory infections (ARI) is crucial for public health prevention and control. Although traditional viral detection methods have partially met clinical needs, they often have limitations such as long detection times, high costs, or limited sensitivity. There is an urgent need for faster and more efficient detection methods. Surface-Enhanced Raman Spectroscopy (SERS) has become a research hotspot in viral detection due to its high sensitivity and specificity. This study aims to develop a novel and efficient detection strategy combining SERS technology with machine learning methods to achieve precise detection of Respiratory Syncytial Virus (RSV), Influenza A Virus (IFA), and Human Adenovirus (HAdV). The study employs citrate-stabilized silver nanoparticles (Ag@cit) and uses iodine ion incubation and calcium ion aggregation to prepare silver nanoparticles (Ag@ICNPs) as the SERS substrate. Ag@ICNPs have high-quality “hotspots” suitable for virus detection, enabling ultra-fast, highly sensitive, and label-free capture of characteristic fingerprint spectra of respiratory viruses. This study integrates machine learning methods with SERS technology to further improve detection efficiency and accuracy. By improving various machine learning algorithms, a virus classifier was successfully established, which can rapidly identify the three viruses within 3 minutes with a detection limit as low as 1.0×102 copies·mL-1and an accuracy rate of 100%. Additionally, the concentration-dependent curves constructed based on the relationship between viral concentration and characteristic peak intensity showed good linearity (R2 greater than 0.998), providing the possibility for quantifying virus content in samples. This is important for monitoring treatment efficacy and disease progression through changes in viral load in clinical settings. This study reveals the significant advantages of the combined application of “SERS@machine learning” in rapidly and precisely detecting respiratory viruses, offering a potentially valuable new approach for ARI clinical diagnosis. It is expected to become an important tool in future clinical diagnosis and public health prevention and control.
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