Seasonal outbreaks of respiratory viral infections remain a global concern, with increasing morbidity and mortality rates recorded annually. Timely and false responses contribute to the widespread of respiratory pathogenic diseases owing to similar symptoms at an early stage and subclinical infection. The prevention of emerging novel viruses and variants is also a big challenge. Reliable point-of-care diagnostic assays for early infection diagnosis play a critical role in the response to threats of epidemics or pandemics. We developed a facile method for specifically identifying different viruses based on surface-enhanced Raman spectroscopy (SERS) with pathogen-mediated composite materials on Au nanodimple electrodes and machine learning (ML) analyses. Virus particles were trapped in three-dimensional plasmonic concave spaces of the electrode via electrokinetic preconcentration, and Au films were simultaneously electrodeposited, leading to the acquisition of intense and in-situ SERS signals from the Au-virus composites for ultrasensitive SERS detection. The method was useful for rapid detection analysis (<15 min), and the ML analysis for specific identification of eight virus species, including human influenza A viruses (i.e., H1N1 and H3N2 strains), human rhinovirus, and human coronavirus, was conducted. The highly accurate classification was achieved using the principal component analysis-support vector machine (98.9%) and convolutional neural network (93.5%) models. This ML-associated SERS technique demonstrated high feasibility for direct multiplex detection of different virus species for on-site applications.
Keywords: Au–virus plasmonic composites; Electrodeposition; Label-free diagnoses; Machine learning; Surface-enhanced Raman spectroscopy.
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