Applying Machine Learning with Localized Surface Plasmon Resonance Sensors to Detect SARS-CoV-2 Particles
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/12/3/173 |
| _version_ | 1797472629044543488 |
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| author | Jiawei Liang Wei Zhang Yu Qin Ying Li Gang Logan Liu Wenjun Hu |
| author_facet | Jiawei Liang Wei Zhang Yu Qin Ying Li Gang Logan Liu Wenjun Hu |
| author_sort | Jiawei Liang |
| collection | DOAJ |
| description | 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<sup>6</sup> 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<sup>2</sup> > 0.95). |
| first_indexed | 2024-03-09T20:04:05Z |
| format | Article |
| id | doaj.art-b2dcff28337842e98a01335c5ec43728 |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-09T20:04:05Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-b2dcff28337842e98a01335c5ec437282023-11-24T00:36:39ZengMDPI AGBiosensors2079-63742022-03-0112317310.3390/bios12030173Applying Machine Learning with Localized Surface Plasmon Resonance Sensors to Detect SARS-CoV-2 ParticlesJiawei Liang0Wei Zhang1Yu Qin2Ying Li3Gang Logan Liu4Wenjun Hu5School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaThe 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<sup>6</sup> 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<sup>2</sup> > 0.95).https://www.mdpi.com/2079-6374/12/3/173SARS-CoV-2machine learningLSPR sensormicroscopic imaging |
| spellingShingle | Jiawei Liang Wei Zhang Yu Qin Ying Li Gang Logan Liu Wenjun Hu Applying Machine Learning with Localized Surface Plasmon Resonance Sensors to Detect SARS-CoV-2 Particles Biosensors SARS-CoV-2 machine learning LSPR sensor microscopic imaging |
| title | Applying Machine Learning with Localized Surface Plasmon Resonance Sensors to Detect SARS-CoV-2 Particles |
| title_full | Applying Machine Learning with Localized Surface Plasmon Resonance Sensors to Detect SARS-CoV-2 Particles |
| title_fullStr | Applying Machine Learning with Localized Surface Plasmon Resonance Sensors to Detect SARS-CoV-2 Particles |
| title_full_unstemmed | Applying Machine Learning with Localized Surface Plasmon Resonance Sensors to Detect SARS-CoV-2 Particles |
| title_short | Applying Machine Learning with Localized Surface Plasmon Resonance Sensors to Detect SARS-CoV-2 Particles |
| title_sort | applying machine learning with localized surface plasmon resonance sensors to detect sars cov 2 particles |
| topic | SARS-CoV-2 machine learning LSPR sensor microscopic imaging |
| url | https://www.mdpi.com/2079-6374/12/3/173 |
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