Surface-Enhanced Raman Scattering (SERS) Spectroscopy Used to Detect COVID-19 Virus


Scientists from Duke University in Durham, North Carolina recently tested a surface-enhanced Raman scattering (SERS) spectroscopy system for detecting the SARS-CoV-2 virus. Their findings were published in Analytical Chemistry (1).

Coronavirus or Flu virus - Microbiology And Virology Concept | Image Credit: © Feydzhet Shabanov -

Coronavirus or Flu virus - Microbiology And Virology Concept | Image Credit: © Feydzhet Shabanov -

The COVID-19 pandemic caused an unprecedented need for rapid, sensitive, and cost-effective point-of-care diagnostic tests to prevent and mitigate the spread of the SARS-CoV-2 virus. Spectroscopy techniques can be used to detect the virus, protecting against the disease, and helping to screening for long COVID. For example, a study out of the Federal University of Sao Francisco Valley in Pernambuco, Brazil tested using visible or near-infrared (Vis-NIR) spectroscopy and machine learning algorithms to detect SARS-CoV-2 (2). Additionally, spectroscopy’s use to detect COVID-19 has been a hot topic at conferences. At SPIE Photonics West 2024 in TK PLACE, for example, Boris Mizaikoff of the University of Ulm discussed using breath analysis with used medical/N95 masks to detect long COVID-19 in patients. “Every time we put on a mask, we’re inhaling air, which means we’re trapping species from the outside world on the outside of the mask. When we’re exhaling, the inside [of the mask] is trapping everything we’re exhaling,” he said when explaining the sentiment behind this research (3).

Read More: Detecting Covid-19 Using Visible or Near-Infrared Spectroscopy and Machine Learning

In this study, the scientists demonstrated an advanced lateral flow immunoassay (LFIA) platform with dual-functional (colorimetric and surface-enhanced Raman scattering [SERS]) detection of the spike 1 (S1) protein of SARS-CoV-2. The nanosensor was integrated with a specially designed core–gap–shell morphology, made of a gold shell decorated with external nanospheres (with the structure being referred to as a gold nanocrown [GNC]). This structure was also labeled with a Raman reporter molecule of 1,3,3,1′,3′,3′-hexamethyl-2,2′-indotricarbocyanine iodide (HITC), so that a strong colorimetric signal and an enhanced SERS signal could be produced.

Read More: Screening for Long COVID Using Exhaled Breath Analysis

Among the different plasmonics-active GNC nanostructures, it was found that the GNC-2 morphology, which has a shell decorated with an optimum number and size of nanospheres, produces an intense dark-blue colorimetric signal and ultrahigh SERS signals. The limit of detection (LOD) of the S1 protein via colorimetric detection LFIA was determined to be 91.24 pg/mL. Compared to the SERS LFIA method, the LOD was found to be over three orders of magnitude lower at 57.21 fg/mL. Additionally, the performance of the GNC-2 nanosensor was analyzed regarding the direct analysis of the S1 protein spiked in saliva samples, albeit without prior sample pretreatment. With this approach, using SERS-based plasmonics-enhanced LFIA, the LOD was found to be as low as 39.65 fg/mL; these findings indicate ultrahigh detection sensitivity. With these findings, the scientists found that their GNC nanosensor exhibited excellent sensitivity, reproducibility, and rapid detection of the SARS-CoV-2 S1 protein. This all demonstrates the technique to have excellent potential as a point-of-care platform for the early detection of respiratory virus infections.


(1) Atta, S.; Zhao, Y.; Li, J. Q.; Vo-Dinh, T. Dual-Modal Colorimetric and Surface-Enhanced Raman Scattering (SERS)-Based Lateral Flow Immunoassay for Ultrasensitive Detection of SARS-CoV-2 Using a Plasmonic Gold Nanocrown. Anal. Chem. 2024, 96 (12), 4783–4790. DOI: 10.1021/acs.analchem.3c04361

(2) Acevedo, A. Detecting Covid-19 Using Visible or Near-Infrared Spectroscopy and Machine Learning. MJH Life Sciences 2023. (accessed 2024-4-25)

(3) Acevedo, A. Screening for Long COVID Using Exhaled Breath Analysis. MJH Life Sciences 2024. (accessed 2024-4-25)

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