Spectroscopic methods for COVID-19 detection and early diagnosis

Abstract

The coronavirus pandemic is a worldwide hazard that poses a threat to millions of individuals throughout the world. This pandemic is caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), which was initially identified in Wuhan, China's Hubei provincial capital, and has since spread throughout the world. According to the World Health Organization's Weekly Epidemiological Update, there were more than 250 million documented cases of coronavirus infections globally, with five million fatalities. Early detection of coronavirus does not only reduce the spread of the virus, but it also increases the chance of curing the infection. Spectroscopic techniques have been widely used in the early detection and diagnosis of COVID-19 using Raman, Infrared, mass spectrometry and fluorescence spectroscopy. In this review, the reported spectroscopic methods for COVID-19 detection were discussed with emphasis on the practical aspects, limitations and applications.

Keywords: COVID-19; Coronavirus; Fluorescence spectroscopy; Infrared spectroscopy; Mass spectrometry; Raman spectroscopy.

Fig. 1

Subtraction Raman spectra of (a) the average CTRL signal versus the average signal of COV + , (b) the average CTRL signal versus the average COV- spectrum and c the average COV + signal versus the average COV − signal. The ± 0.005 ΔI intervals are indicated in the graphs, confirmed by the error propagation from the spectra standard deviation. d Overlapped average spectra of CTRL, COV + and COV − with the main different regions highlighted. (With permission from [16])

Fig. 2

a SEM image of the Au–Cu nano, stars used as SERS substrate. The image shows very sharp peaks which act as plasmonic antennas. b Optical absorption spectra of the nano, stars revealed a strong peak in the near IR. (With permission from [44])

Fig. 3

a Average raw spectra and b pre-processed spectra for saliva (n = 2), pure COVID-19 virus in different concentrations (n = 28, 1 × 10⁵–98 copies/mL), and saliva + virus in different concentrations (n = 63, 1 × 10⁵–24 copies/mL). c PCA scores and d PCA loadings on PC1 vs. PC2 for the pre-processed data. Inset c1 and c2 show both low concentration (≤ 781 copies/mL) and high concentration (≥ 1.25 × 10.⁴ copies/mL) of mixed saliva/virus. (With permission from [49])

Fig. 4

Detection of SARS-CoV-2 with multivariable analysis. a Raw Spectra b Sample of 2nd derivative of Savitzky–Golay smoothened spectra of positive and negative samples. c, d, e First three latent variables of PLS-DA. (f) Coefficients of variables selected by the sparse classification algorithm of second derivatives of raw spectra (g, h) Zooms on regions indicated by sparse classification. i Projection of the 280 spectra used according to the first two latent variables obtained. j Projection of the 280 spectra used according to the first three latent variables obtained. (With permission from [53])

Fig. 5

Mean of FTIR spectra of healthy (N = 1209) and COVID-19 (N = 255) groups. A Biological fingerprint region, diverse absorption bands related to biological samples are evidenced such as Amide I (1644 cm⁻¹), Amide II (1545 cm⁻¹), and Amide III (1240 cm⁻¹), as well as phosphorylated molecules (1240 cm⁻¹and 1076 cm⁻¹), carbohydrates (1030 cm⁻¹), and DNA backbone (968 cm⁻¹). Likewise, peaks in ranges: 1100–850 cm⁻¹and 1080–950 cm⁻¹ attributed to nucleic acids and α-amylase, respectively were observed. Differences in absorbance and displacements between the bands of the groups representing changes in biochemical compositions were evidenced. B Immunoglobulins regions, different intervals were detected such as IgG (560–1464 cm⁻¹), IgM (1420–1289 cm⁻¹ and 1160–1028 cm⁻¹), and IgA (1285–1237 cm.⁻¹), noticing that the COVID-19 group exhibited a higher absorbance. (With permission from [54])

Fig. 6

a Proposed microfluidic platform for virus detection. b Proposed VACNTs functionalized microfluidic platform. (With permission from [14])