Ultra-rapid on-site detection of SARS-CoV-2 infection using simple ATR-FTIR spectroscopy and analysis algorithm: high sensitivity and specificity (preprint)

ABSTRACT

There is an urgent need for ultra-rapid testing regimens to detect the SARS-CoV-2 [Severe Acute Respiratory Syndrome Coronavirus 2] virus infections in real-time within seconds to stop its spread. Current testing approaches for this RNA virus focus primarily on diagnosis by RT-qPCR, which is time-consuming, costly, often inaccurate and impractical for general population rollout due to the need for laboratory processing. The latency until the test result arrives with the patient has led to further virus spread. Furthermore, latest antigen rapid tests still require 15 to 30 min processing time and are challenging to handle. Despite increased PCR-test and antigen-test efforts the pandemic has entered the worldwide second stage. Herein, we applied a superfast reagent-free and non-destructive approach of attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy with subsequent chemometric analysis to the interrogation of virus-infected samples. Contrived samples with inactivated gamma-irradiated Covid-19 virus particles at levels down to 1582 copies/ml generated infrared (IR) spectra with good signal-to-noise ratio. Predominant virus spectral peaks are associated with nucleic acid bands, including RNA. At low copy numbers, the presence of virus particle was found to be capable of modifying the IR spectral signature of saliva, again with discriminating wavenumbers primarily associated with RNA. Discrimination was also achievable following ATR-FTIR spectral analysis of swabs immersed in saliva variously spiked with virus. Following on, we nested our test system in a clinical setting wherein participants were recruited to provide demographic details, symptoms, parallel RT-qPCR testing and the acquisition of pharyngeal swabs for ATR-FTIR spectral analysis. Initial categorisation of swab samples into negative versus positive Covid-19 infection was based on symptoms and PCR results. Following training and validation of a genetic algorithm-linear discriminant analysis (GA-LDA) algorithm, a blind sensitivity of 95% and specificity of 89% was achieved. This prompt approach generates results within two minutes and is applicable in areas with increased people traffic that require sudden test results such as airports, events or gate controls.