Intelligent Surface-Enhanced Raman Scattering Sensor System for Virus Identification

COVID-19 has devastated the entire world for the past couple of years. Timely and efficient detection and identification of a virus are crucial in preventing the wider virus spread. By using intelligent sensors based on Surface-Enhanced Raman Scattering (SERS), it is possible to detect and identify virus automatically. In this study, we successfully applied the XGBoost Algorithm (Supervised Machine Learning) to classify the type of the virus using the SERS sensor data. The supervised approach has a limitation when a new type of virus arises, whose shape is different from the previously known samples. To tackle this problem, we investigated the unsupervised learning approaches that can cluster the virus data into different groups without labeled data. The unsupervised approach presented in this paper is called k-Shape Clustering. This technique compares the cross-correlation between different samples and then clusters them into similar or different groups. If a subvariant of a virus emerges, it would be clustered into the existing virus groups; if a new type of virus is found, it would be clustered into a new group. Both of the approaches have shown very promising results based on extensive evaluations.