Evaluation of Synthetic Raman Spectra for Use in Virus Detection

A Generative Adversarial Network was used to produce Raman spectra of Influenza A virus in culture and then used to train a virus detection classification model. Dimensionality reduction plotting using t-Distributed Stochastic Neighbor Embedding (t-SNE) demonstrated overlap between the real and synthetic spectra but not complete blending, which can be attributed to the subtle differences between the real and synthetic data. Nevertheless, the real and synthetic spectra also exhibited similar Raman peak patterns. Moreover, the inclusion of synthetic spectra into the training set was able to increase the virus classification accuracy from 83.5% to 91.5%. This indicates that the GANs were able to synthesize spectra closely related to virus-positive spectra yet distinctly different from virus-negative spectra, which appear visually similar. We conclude that the synthetic spectra produced by the GANs were similar to the real data but not an exact replacement.