Viral outbreaks such as SARS-CoV, MERS-CoV, and COVID-19 underscore the urgent need for rapid, sensitive, and scalable diagnostic technologies. Current standard methods, including nucleic acid amplification tests and immunoassays, offer complementary strengths but face limitations in cost, turnaround time, and early-stage detection. Surface-enhanced Raman spectroscopy (SERS) has emerged as a promising alternative, leveraging plasmonic nanostructures to amplify weak Raman signals and provide molecular “fingerprints” of viral components with single-molecule sensitivity. This review provides a comprehensive overview of SERS-based virus detection, highlighting the fundamental principles of Raman enhancement, the role of substrates and hotspots, and analyte-specific challenges. We categorize sensing strategies into direct detection of intact viruses and components, affinity-based approaches using antibodies, aptamers, or viral receptors, and labeled methods that amplify specificity and multiplexing. Advances in nanofabrication, receptor engineering, and machine learning have significantly improved sensitivity, reproducibility, and classification accuracy, with detection limits reaching down to a few viral particles per milliliter. Despite these advances, challenges remain in handling biological complexity, ensuring reproducibility, and translating assays into clinical practice. We conclude by outlining opportunities for integrating SERS with portable devices, standardized spectral libraries, and artificial intelligence, paving the way toward rapid, robust, and deployable viral diagnostics for future pandemic preparedness.
Yanjun Yang, Yiping Zhao· Chemical Reviews· 0 citations
Label-free surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) provides a rapid, reagent-light approach to microbial detection, identification, and phenotyping. Translation remains limited, however, by variability in sample preparation, substrates, acquisition conditions, preprocessing, and validation design. We conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided, multi-database review of 129 eligible experimental studies, published predominantly between 2021 and 2026, spanning bacterial, viral, fungal, other microbial, and cross-domain applications. The literature was analyzed as an end-to-end workflow comprising target and matrix definition, sample handling, SERS platform design, spectral acquisition and preprocessing, computational modeling and interpretation, and validation. We synthesized trends in substrates, capture and enrichment, chemometrics, classical ML, deep learning, generative modeling, and explainable artificial intelligence, together with emerging applications in antimicrobial resistance, multiplexing, co-infection, and variant or genotype discrimination. A corpus-level audit showed that controlled-development datasets accounted for 68.2% of studies, 62.0% included no more than 25 independent biological units, and large spectral datasets frequently reflected intensive within-sample replication. Moreover, 68.2% lacked biological-unit-aware predictive validation, whereas only 6.2% performed external or major-domain validation. These findings indicate that spectral volume and model complexity alone are insufficient measures of evidential strength. Progress toward deployable microbial-SERS-ML requires biologically independent evaluation, leakage-safe preprocessing, reproducible substrates and sampling workflows, calibration across instruments and matrices, and prospective multi-batch, multi-site validation.
S. Mahmud, Md. Sakib Bin Islam, M. Naznine et al.· Talanta: The International J...· 0 citations
