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In this study, we explored machine learning approaches for predictive diagnosis using surface-enhanced Raman scattering (SERS), applied to the detection of COVID-19 infection in biological samples. To do this, we utilized SERS data collected from 20 patients at the University of Maryland Baltimore School of Medicine. As a preprocessing step, the positive-negative labels are obtained using Polymerase Chain Reaction (PCR) testing. First, we compared the performance of linear and nonlinear dimensionality techniques for projecting the high-dimensional Raman spectra to a low-dimensional space where a smaller number of variables defines each sample. The appropriate number of reduced features used was obtained by comparing the mean accuracy from a 10-fold cross-validation. Finally, we employed Gaussian process (GP) classification, a probabilistic machine learning approach, to correctly predict the occurrence of a negative or positive sample as a function of the low-dimensional space variables. As opposed to providing rigid class labels, the GP classifier provides a probability (ranging from zero to one) that a given sample is positive or negative. In practice, the proposed framework can be used to provide high-throughput rapid testing, and a follow-up PCR can be used for confirmation in cases where the model’s uncertainty is unacceptably high. 

Abstract Liquid interfaces facilitate the organization of nanometer‐scale biomaterials with plasmonic properties suitable for molecular diagnostics. Using hierarchical assemblage of 2D hafnium disulfide nanoplatelets and zero‐dimensional spherical gold nanoparticles, the design of a multifunctional material is reported. When the target analyte is present, the nanocomposites’ self‐assembling pattern changes, altering their plasmonic response. Using monkeypox virus (MPXV) as an example, the findings reveal that adding genomic DNA to the nanocomposite surface increases the agglomeration between gold nanoparticles and decreases the π‐stacking distance between hafnium disulfide nanoplatelets. Further, this self‐assembled nanomaterial is found to have minimal cross‐reactivity toward other pathogens and a limit of detection of 7.6 pg µL⁻¹(i.e., 3.57 × 10⁴copies µL⁻¹) toward MPXV. Overall, this study helped to gain a better understanding of the genomic organization of MPXV to chemically design and develop targeted nucleotides. The study has been validated by UV–vis spectroscopy, X‐ray diffraction, scanning transmission electron microscopy, surface‐enhanced Raman microscopy and electromagnetic simulation studies. To the best knowledge, this is the first study in literature reporting selective molecular detection of MPXV within a few minutes and without the use of any high‐end instrumental techniques like polymerase chain reactions.