A concise discussion on the potential spectral tools for the rapid COVID-19 detection

• Published in: Results in Chemistry 2021-01, Vol.3, p.100138-100138, Article 100138
• Main Authors: Mohanty, Abhijeet, Fatrekar, Adarsh P., Krishnan, Saravanan, Vernekar, Amit A.
• Format: Article
• Language: English
• Subjects: COVID-19; Machine learning; MALDI-MS; RT-LAMP; RT-PCR; SARS-CoV-2
• ISSN: 2211-7156; 2211-7156
• DOI: 10.1016/j.rechem.2021.100138

•The urgency in quick and effective diagnosis of COVID-19 is highlighted.•Pitfalls in the conventional RT-PCR and ELISA assay are discussed.•Working protocol of MALDI-MS and RT-LAMP spectral tools in COVID-19 diagnosis are summarized.•Significance of ML and AI incorporation with the spectral tools is highlighted.•Future scope in further development of these spectral tools are discussed. Developing robust methods to detect the severe acute respiratory syndromecoronavirus-2 (SARS-CoV-2), a causative agent for the current global health pandemic, is an exciting area of research. Nevertheless, the currently used conventional reverse transcription-polymerase chain reaction (RT-PCR) technique in COVID-19 detection endures with some inevitable limitations. Consequently, the establishment of rapid diagnostic tools and quick isolation of infected patients is highly essential. Furthermore, the requirement of point-of-care testing is the need of the hour. Considering this, we have provided a brief review of the use of very recently reported robust spectral tools for rapid COVID-19 detection. The spectral tools include, colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), with the admittance of principal component analysis (PCA) and machine learning (ML) for meeting the high-throughput and fool-proof platforms for the detection of SARS-CoV-2, are reviewed. Recently, these techniques have been readily applied to screen a large number of suspected patients within a short period and they demonstrated higher sensitivity for the detection of COVID-19 patients from unaffected human subjects.