The Label-Free Detection and Identification of SARS-CoV-2 Using Surface-Enhanced Raman Spectroscopy and Principal Component Analysis

Abstract

The World Health Organization (WHO) declared in a May 2023 announcement that the COVID-19 illness is no longer categorized as a Public Health Emergency of International Concern (PHEIC); nevertheless, it is still considered an actual threat to world health, social welfare and economic stability. Consequently, the development of a convenient, reliable and affordable approach for detecting and identifying SARS-CoV-2 and its emerging new variants is crucial. The fingerprint and signal amplification characteristics of surface-enhanced Raman spectroscopy (SERS) could serve as an assay scheme for SARS-CoV-2. Here, we report a machine learning-based label-free SERS technique for the rapid and accurate detection and identification of SARS-CoV-2. The SERS spectra collected from samples of four types of coronaviruses on gold nanoparticles film, fabricated using a Langmuir–Blodgett self-assembly, can provide more spectroscopic signatures of the viruses and exhibit low limits of detection (<100 TCID50/mL or even <10 TCID50/mL). Furthermore, the key Raman bands of the SERS spectra were systematically captured by principal component analysis (PCA), which effectively distinguished SARS-CoV-2 and its variant from other coronaviruses. These results demonstrate that the combined use of SERS technology and PCA analysis has great potential for the rapid analysis and discrimination of multiple viruses and even newly emerging viruses without the need for a virus-specific probe.

Fractal Plasmonic Molecule for Multi-Sensing: SERS Platform for SARS-CoV-2 Detection

“…84 Our system shows an LoD in some cases similar or even competitive with those reported. 84 Our aim with this test was to demonstrate the potential in terms of sensing of the proposed FPM and its performance that can be further improved, for example, by testing different functionalization processes. However, the value for the LoD estimated for our system of 88 PFU/mL is about 4−5 times lower than that of a commercial lateral flow assay (LFA) strip test for SARS-CoV-2 (300−500 PFU/ mL).…”

“…We point out here that it is not a purpose of this work to realize the most sensitive assay for SARS-CoV-2 with best-performing of the LoD as those reported in literature even realized with different technologies . Our system shows an LoD in some cases similar or even competitive with those reported .…”

“…We point out here that it is not a purpose of this work to realize the most sensitive assay for SARS-CoV-2 with best-performing of the LoD as those reported in literature even realized with different technologies . Our system shows an LoD in some cases similar or even competitive with those reported . Our aim with this test was to demonstrate the potential in terms of sensing of the proposed FPM and its performance that can be further improved, for example, by testing different functionalization processes.…”

“…Our aim with this test was to demonstrate the potential in terms of sensing of the proposed FPM and its performance that can be further improved, for example, by testing different functionalization processes. However, the value for the LoD estimated for our system of 88 PFU/mL is about 4–5 times lower than that of a commercial lateral flow assay (LFA) strip test for SARS-CoV-2 (300–500 PFU/mL). − Preliminary measurements to test the specificity of our system for the detection of SARS-CoV-2 were made using a different nontarget virus. Figure d reports the SERS spectra measured for the antibody alone (black line) and after incubation of 8.9 × 10 5 PFU/mL of the influenza virus BCoV 1 (OC43) in culture medium (blue line).…”

“…Furthermore, we want to underline that the multisensing properties demonstrated in the previous sections for the FPM investigated can open up to their future development for advanced simultaneous detection of multiple viruses/analytes (multiplexing detection) supported by new analysis approaches based on artificial intelligence, machine learning, and deep learning-based technologies. , …”