ArticleScientific reports2021
COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed.
- Combining label-free Raman spectroscopy with machine learning to monitor COVID-19 disease from acute infection to recovery.Journal of biomedical optics · 2026Article
- Early Sepsis Diagnosis as a Global Imperative: The Role of Raman Spectroscopy.Journal of clinical medicine · 2026Review
- Combining label-free Raman spectroscopy and machine learning to identify early biomarkers of COVID-19 disease severity and mortality.Journal of biomedical optics · 2026Article
- Recent Advances in Raman Spectral Classification with Machine Learning.Sensors (Basel, Switzerland) · 2026Review
- Development of a Raman-Based Method for the Diagnosis of People with Obstructive Sleep Apnea Syndrome: The Role of Lactic Acid.International journal of molecular sciences · 2025Article
- Current Trends in In Vitro Diagnostics Using Surface-Enhanced Raman Scattering in Translational Biomedical Research.Biosensors · 2025Review
- Emerging Trends of Gold Nanostructures for Point-of-Care Biosensor-Based Detection of COVID-19.Molecular biotechnology · 2025Review
- Detection of Respiratory Disease Based on Surface-Enhanced Raman Scattering and Multivariate Analysis of Human Serum.Diagnostics (Basel, Switzerland) · 2025Article
- Innovative development of territories based on the integrated use of social, resource and environmental potential.Scientific reports · 2024Article
- Review
- Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification.PNAS nexus · 2024Article
- Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy.Biomedicines · 2024Article
- Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review.International journal of molecular sciences · 2023Review
- Diagnosing COVID-19 in nasopharyngeal secretion through Raman spectroscopy: a feasibility study.Lasers in medical science · 2023Article
- Nano-biosensor for SARS-CoV-2/COVID-19 detection: methods, mechanism and interface design.RSC advances · 2023Review
- Far-field super-resolution chemical microscopy.Light, science & applications · 2023Review
- SERS Signature of SARS-CoV-2 in Saliva and Nasopharyngeal Swabs: Towards Perspective COVID-19 Point-of-Care Diagnostics.International journal of molecular sciences · 2023Article
- New, fast, and precise method of COVID-19 detection in nasopharyngeal and tracheal aspirate samples combining optical spectroscopy and machine learning.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2023Article
- Simple, Fast and Convenient Magnetic Bead-Based Sample Preparation for Detecting Viruses via Raman-Spectroscopy.Biosensors · 2023Article
- Detection of live SARS-CoV-2 virus and its variants by specially designed SERS-active substrates and spectroscopic analyses.Analytica chimica acta · 2023Article
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The pandemic of COVID-19 is continuously spreading, becoming a worldwide emergency. Early and fast identification of subjects with a current or past infection must be achieved to slow down the epidemiological widening. Here we report a Raman-based approach for the analysis of saliva, able to significantly discriminate the signal of patients with a current infection by COVID-19 from healthy subjects and/or subjects with a past infection. Our results demonstrated the differences in saliva biochemical composition of the three experimental groups, with modifications grouped in specific attributable spectral regions. The Raman-based classification model was able to discriminate the signal collected from COVID-19 patients with accuracy, precision, sensitivity and specificity of more than 95%. In order to translate this discrimination from the signal-level to the patient-level, we developed a Deep Learning model obtaining accuracy in the range 89-92%. These findings have implications for the creation of a potential Raman-based diagnostic tool, using saliva as minimal invasive and highly informative biofluid, demonstrating the efficacy of the classification model.
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