Multiplex Detection and Quantification of Virus Co-Infections Using Label-free Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms

作者：Yanjun Yang, Jiaheng Cui, Amit Kumar, Dan Luo, Jackelyn Murray, Les Jones, Xianyan Chen, Sebastian Hülck, Ralph A. Tripp, Yiping Zhao · 发表于：ACS Sensors · 年份：2025 · DOI：10.1021/acssensors.4c03209 · 被引用次数：29 · 研究领域：Spectroscopy Techniques in Biomedical and Chemical Research、SARS-CoV-2 detection and testing、Biosensors and Analytical Detection

Multiple respiratory viruses can concurrently or sequentially infect the respiratory tract, making their identification crucial for diagnosis, treatment, and disease management. We present a label-free diagnostic platform integrating surface-enhanced Raman scattering (SERS) with deep learning for rapid, quantitative detection of respiratory virus coinfections. Using sensitive silica-coated silver nanorod array substrates, over 1.2 million SERS spectra are collected from 11 viruses, nine two-virus mixtures, and four three-virus mixtures at various concentrations in saliva. A deep learning model, MultiplexCR, is developed to simultaneously predict virus species and concentrations from SERS spectra. It achieves an impressive 98.6% accuracy in classifying virus coinfections and a mean absolute error of 0.028 for concentration regression. In blind tests, the model demonstrates consistent high accuracy and precise concentration predictions. This SERS-MultiplexCR platform completes the entire detection process in just 15 min, offering significant potential for rapid, point-of-care diagnostics in infection detection, as well as applications in food safety and environmental monitoring.