There
is an urgent need for ultrarapid testing regimens to detect
the severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] 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 15–30 min processing time and are challenging to handle.
Despite increased polymerase chain reaction (PCR)-test and antigen-test
efforts, the pandemic continues to evolve worldwide. Herein, we developed
a superfast, reagent-free, and nondestructive approach of attenuated
total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy
with subsequent chemometric analysis toward the prescreening of virus-infected
samples. Contrived saliva samples spiked with inactivated γ-irradiated
COVID-19 virus particles at levels down to 1582 copies/mL generated
infrared (IR) spectra with a good signal-to-noise ratio. Predominant
virus spectral peaks are tentatively associated with nucleic acid
bands, including RNA. At low copy numbers, the presence of a 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.
Next, 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 categorization of swab samples into negative
versus positive COVID-19 infection was based on symptoms and PCR results
(
n
= 111 negatives and 70 positives). Following training
and validation (using
n
= 61 negatives and 20 positives)
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 2 min and is applicable in
areas with increased people traffic that require sudden test results
such as airports, events, or gate controls.