4.7 Article

Applying Machine Learning with Localized Surface Plasmon Resonance Sensors to Detect SARS-CoV-2 Particles

Journal

BIOSENSORS-BASEL
Volume 12, Issue 3, Pages -

Publisher

MDPI
DOI: 10.3390/bios12030173

Keywords

SARS-CoV-2; machine learning; LSPR sensor; microscopic imaging

Funding

  1. National Nature Science Foundation of China [82072735]
  2. Major Research Program of the National Natural Science Foundation of China [91959107]
  3. Fundamental Research Funds for the Central Universities [2019kfyXMPY002]
  4. National Key R&D Program of China [2020YFC0861900]

Ask authors/readers for more resources

This study developed a novel method using localized surface plasmon resonance (LSPR) sensors, optical imaging, and artificial intelligence methods to directly detect SARS-CoV-2 virus particles. This method requires no sample preparation and can qualitatively and quantitatively detect virus concentration with an accuracy of over 97%.
The sudden outbreak of COVID-19 rapidly developed into a global pandemic, which caused tens of millions of infections and millions of deaths. Although SARS-CoV-2 is known to cause COVID-19, effective approaches to detect SARS-CoV-2 using a convenient, rapid, accurate, and low-cost method are lacking. To date, most of the diagnostic methods for patients with early infections are limited to the detection of viral nucleic acids via polymerase chain reaction (PCR), or antigens, using an enzyme-linked immunosorbent assay or a chemiluminescence immunoassay. This study developed a novel method that uses localized surface plasmon resonance (LSPR) sensors, optical imaging, and artificial intelligence methods to directly detect the SARS-CoV-2 virus particles without any sample preparation. The virus concentration can be qualitatively and quantitatively detected in the range of 125.28 to 10(6) vp/mL through a few steps within 12 min with a limit of detection (LOD) of 100 vp/mL. The accuracy of the SARS-CoV-2 positive or negative assessment was found to be greater than 97%, and this was demonstrated by establishing a regression machine learning model for the virus concentration prediction (R-2 > 0.95).

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available