Fast Screening and Primary Diagnosis of COVID-19 by ATR FT-IR

Full text

Fast Screening and Primary Diagnosis of COVID-19 by ATR−FT-IR
Liyang Zhang,
∥
Meng Xiao,
∥
Yao Wang,
∥
Siqi Peng, Yu Chen, Dong Zhang, Dongheyu Zhang,
Yuntao Guo, Xinxin Wang, Haiyun Luo,*Qun Zhou,*and Yingchun Xu*
Cite This: Anal. Chem. 2021, 93, 2191−2199
Read Online
ACCESS Metrics & More Article Recommendations *
sıSupporting Information
ABSTRACT: The outbreak of coronavirus disease 2019 (COVID-19) has led to
substantial infections and mortality around the world. Fast screening and diagnosis
are thus crucial for quick isolation and clinical intervention. In this work, we
showed that attenuated total reflection−Fourier transform infrared spectroscopy
(ATR−FT-IR) can be a primary diagnostic tool for COVID-19 as a supplement to
in-use techniques. It requires only a small volume (∼3μL) of the serum sample
and a shorter detection time (several minutes). The distinct spectral differences
and the separability between normal control and COVID-19 were investigated
using multivariate and statistical analysis. Results showed that ATR−FT-IR
coupled with partial least squares discriminant analysis was effective to differentiate
COVID-19 from normal controls and some common respiratory viral infections or
inflammation, with the area under the receiver operating characteristic curve
(AUROC) of 0.9561 (95% CI: 0.9071−0.9774). Several serum constituents
including, but not just, antibodies and serum phospholipids could be reflected on the infrared spectra, serving as “chemical
fingerprints”and accounting for good model performances.
■INTRODUCTION
Coronavirus disease 2019 (COVID-19) is a pandemic caused
by severe acute respiratory syndrome coronavirus 2 (SARS-
CoV-2), a newly appearing coronavirus which has spread over
the world and led to substantial infections and mortality.
1
Reverse transcription polymerase chain reaction (RT-PCR)
is a conventional and standard assay for viral diagnosis and has
been widely used for SARS-CoV-2 RNA detection. SARS-
CoV-2 RNA can be detected in both upper and lower
respiratory specimens including nasal swab, oropharyngeal
swab, sputum, and bronchoalveolar lavage fluid (BALF).
2,3
Despite BALF, which is not a requisite for COVID-19
diagnosis because of the harder sampling, the sputum was
reported to have the highest positive rate (74.4−88.9%),
followed by nasal swabs (53.6−73.3%) during the first 14 days
after onset (d.a.o.).
3
The positive rate for throat swab was
reported to be around 60%.
3,4
Viral RNA is also detected in
serum samples with a percentage of 0% (0/31),
5
8% (1/12),
6
or 15% (6/41).
7
Notably, several factors may influence the
performance of RT-PCR such as improper sample preparation
or varied qualities of detection kits and thus lead to high false-
positive rates. In addition, viral replication is inhibited in the
late stage of infection, accounting for the high false-negative
rates in this stage. Also, it is time-consuming to perform the
whole test procedures.
Serological assay based on immunoglobulin-G (IgG) and
IgM levels can serve as a complement to nucleic acid
detection.
8,9
The median time of IgM and IgG seroconversion
was reported to be 5 (n= 41) and 14 (n= 208) days after
onset, respectively.
10
The combination of IgM and IgG tests
yielded a higher detection sensitivity of 88.66% and specificity
of 90.63% (397 PCR confirmed patients and 128 negative
patients in total) than a single IgG or IgM test.
11
Additionally,
a higher positive detection rate of 99.4% (n= 173, 95% CI
96.8−100%) was achieved when applying both antibody and
nucleic acid tests, compared to a single RNA test of 67.1%
(95% CI 59.4−74.1%).
12
Nevertheless, there remain some
problems unclear such as the antibody responses of COVID-19
patients, the potential false positive caused by immunological
cross reactivity, and the varied performances of commercially
available detection kits.
Rapid and reliable diagnosis of COVID-19 is of great
significance to help screen the COVID-19 patients and deliver
more appropriate treatment. In the last decade, transmission or
attenuated total reflection (ATR) Fourier transform infrared
spectroscopy (FT-IR) and Raman spectroscopy have been
utilized to identify viral infections or predict viral load in
blood,
13
sera,
13−15
plasma,
16
or infected cells,
17,18
differentiate
different viral infections,
19
and verify the infectious agent type
(as bacterial or viral) based on the white blood cell data.
20
Subtle molecular and chemical changes in blood components
Received: September 25, 2020
Accepted: December 18, 2020
Published: January 11, 2021
Articlepubs.acs.org/ac
© 2021 American Chemical Society 2191
https://dx.doi.org/10.1021/acs.analchem.0c04049
Anal. Chem. 2021, 93, 2191−2199
This article is made available via the ACS COVID-19 subset for unrestricted RESEARCH re-use
and analyses in any form or by any means with acknowledgement of the original source.
These permissions are granted for the duration of the World Health Organization (WHO)
declaration of COVID-19 as a global pandemic.
Downloaded via 223.72.51.182 on March 14, 2021 at 05:57:33 (UTC).
See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

in response to bacterial or viral infections can be recorded and
reflected by the infrared spectra. For example, the strong band
at 1631 cm−1attributed to the β-pleated sheet protein marker
of Ig is unique to the positive serum spectra induced by
hepatitis B and C virus.
14
In comparison with other assays,
infrared spectroscopy enables us to inspect almost all biological
components at once, which may be beneficial to COVID-19
diagnosis. Additionally, it is easier to perform and takes less
operation time (typically for several minutes).
In this work, we showed the feasibility of ATR−FT-IR in
COVID-19 screening and primary diagnosis. The spectral
differences between COVID-19 and healthy controls and the
potential spectral markers were identified by multivariate and
statistical analysis. For the purpose of the performance test,
especially the specificity of the proposed model, healthy
controls and some common respiratory viral infections or
inflammation were considered.
■MATERIALS AND METHODS
Participants. We collected a total of 115 blood samples
from 20 healthy donors and 76 patients, of which 41 were
confirmed with COVID-19, 15 had respiratory viral infections
caused by influenza A/B or respiratory syncytial virus (RSV),
and 20 were with inflammation-related diseases (Table 1).
Influenza A/B and RSV are chosen because they are common
in respiratory infections and share similar flu-like symp-
toms
21,22
to COVID-19. Based on a report regarding 1099
patients, the most common symptoms of COVID-19 are fever
(43.8% on admission and 88.7% during hospitalization) and
cough (67.8%).
23
We know that inflammation-related diseases
can also contribute to alterations in serum components (see
Discussion). Here, respiratory bacterial infections, pulmonary
infection, intra-abdominal infection, bacteremia, and some
other diseases were enrolled in the study (Table S1). We aim
to investigate whether and how the serum infrared spectra are
specific for COVID-19.
The COVID-19 patients were from two cohorts, all of whom
were diagnosed by reverse transcriptase PCR (RT-PCR)
following China national guidelines for diagnosis and treatment of
Corona Virus Disease 2019 (COVID-19) (trial version 5,
revised).
24
The first cohort included 35 critically ill COVID-19
patients who were admitted from Feb 15 to March 30 at the
Sino-French New City Branch of Tongji Hospital in Wuhan.
Of the 35 patients, one was with <7 days postsymptom onset
and one was within 7−14 days, while 33 were with >14 days.
In the second cohort collected in the Peking Union Medical
College Hospital, five confirmed cases were with >14 days after
onset, while one was with 2 days. All of them were with mild
symptoms. Clinical residual blood samples (stored at −80 °C)
from influenza A, influenza B, or respiratory syncytial virus
(RSV)-infected patients admitted from Feb 16 to March 1,
2020, were revived. The viral nucleic acids were determined by
X’Pert Xpress Flu/RSV (Cepheid AB, Sweden). Of note, for
each patient with influenza A or RSV infections, two to three
blood specimens from different infection stages before
recovery were collected to enrich and generalize the data set.
Patients with other diseases were diagnosed using standard
clinical methods and proved to be without any SARS-CoV-2
infection (Table S1). This study was approved by the Ethics
Committees from Tsinghua University and Peking Union
Medical College Hospital.
Sample Preparation and ATR−FTIR Spectroscopy.
The serum samples were obtained by blood centrifugation
under 5000 rpm for 5 min. Prior to measurements, the serum
specimens were incubated at 56 °C for 30 min to inactivate the
potential pathogens. Each specimen was measured one to three
times on a PerkinElmer infrared spectrometer coupled with a
diamond ATR accessory at a resolution of 4 cm−1. Sixteen
scans were accumulated per spectrum. For each measurement,
an aliquot of 3 or 4 μL serum sample was transferred onto the
ATR crystal and allowed to dry under mild airflow at room
temperature. Water absorption was monitored by OH
stretching at around 3300 cm−1and bending at around 1635
cm−1. It took about 3−5 min for samples to be sufficiently
dried. Afterward, the spectra between 4000 and 600 cm−1were
collected. The spectral background was recorded separately for
each sample to achieve a higher signal-to-noise ratio. Prior to
data analysis, the raw spectral data were preprocessed with
baseline calibration using the built-in rubber-band algorithm
on the PerkinElmer spectrum (version 10.5.4). The corre-
sponding constant baseline drift at 4000 cm−1was subtracted
for each sample spectrum, and then, all the spectra were
normalized to amide I by peak absorbance. The second-
derivative infrared (SD-IR) spectra were calculated from the
normalized spectra.
Multivariate and Statistical Analysis. All data analyses
were performed on MATLAB (version R2020b, Mathworks,
Natick, U.S.A). The second-derivative infrared (SD-IR)
spectra were calculated by the 13-point Savitzky−Golay
algorithm using in-house protocols.
Analysis of variance (ANOVA) is a well-established method
to evaluate the difference of the observed statistic(s) among
groups by calculating the F-statistic (i.e., the ratio of between-
group variance to within-group variance).
25
The larger F-
statistic yields a lower pvalue, indicating that the groups are
more likely to be different in this statistic. In this work, the p
value was used to evaluate the statistical spectral differences of
major bands in absorbances or band locations of healthy
controls and COVID-19 patients. Hence, the potential spectral
markers can be primarily identified.
Hierarchical cluster analysis (HCA) and principal compo-
nent analysis (PCA) are widely used unsupervised approaches
in microorganism differentiation.
26−28
In HCA, the spectral
distance (usually Euclidian distance) is calculated to construct
the hierarchical structure, in which root nodes are formed by
spectra with the shortest distances, while leaf nodes are formed
by the longest distances. PCA aims to reduce the dimensions
of the original matrix data in terms of maximum variance.
26,29
In the new vector space formed by principal components
(PC), data distribution is more apparent. Here, the natural
Table 1. Number of Participants, Specimens, and the
Measured Spectra in This Study
participants
(n= 96) specimens
(n= 115) spectra
(n= 289)
COVID-19
(cohort 1) 35 35 59
COVID-19
(cohort 2) 6618
Influenza A 4 8 23
Influenza B 2 2 5
RSV 9 24 73
inflammation
a
20 20 60
control 20 20 51
a
Details are presented in Table S1.
Analytical Chemistry pubs.acs.org/ac Article
https://dx.doi.org/10.1021/acs.analchem.0c04049
Anal. Chem. 2021, 93, 2191−2199
2192

separability of normal controls and nonsevere and severe
COVID-19 patients was tested by the two methods.
Partial least squares discriminant analysis (PLS-DA) is a
supervised chemometric technique whereby the so-called
latent variables are successively extracted to find the maximum
correlation between the X-matrix and Y-matrix.
14,30
In the
present study, the data set was divided into three groups:
normal controls, COVID-19, and infections caused by other
diseases. PLS1 and PLS2 models
31
were applied for two-group
and three-group classification, respectively. Specifically, in the
case of PLS1, the two classes were coded as [0] and [1],
whereas the three classes were coded as [1 0 0], [0 1 0], and [1
0 0] in the PLS2 model. In comparison with PCA, PLS-DA is
more suitable to identify the significant wavenumbers for
discrimination by inspecting the regression vector
14
or the
variable importance in projection (VIP).
32
■RESULTS
COVID-19 Vs Controls: Band Assignments, Spectral
Differences, and Spectral Interpretation. It is known that
serum is composed of proteins, cholesterol, glucose, urea,
triglycerides, and other more dilute compounds, all of which
can be recorded in the spectra, whereas only components with
higher abundance could be identified in the spectra and
provide insightful information. Reports show that a wide range
of abundant biomolecules in plasma can be quantified using
FTIR.
33
To better illustrate the spectral features, we
summarized the normal levels of the major serum components
(Table S2).
Averaged spectra minimize the influence of individual
differences and thus are more representative. As for the
original spectra, all bands had significant frequency shifts in the
COVID-19 group (p< 0.001, Figure 1a and Table 2),
Figure 1. Spectral profiles of COVID-19 and control serum samples and some abundant constituents in human serum. (a) Averaged original
spectra of COVID-19 and control samples. Spectra were normalized to amide I. The principal absorbance bands were annotated. (b) Averaged SD-
IR spectra of COVID-19 and control samples and the most abundant constituents in human serum. Bands with notable variations were annotated
(see also Table S3). Lysolecithin, sphingomyelin, lecithin, and human IgG (Bioss, Beijing, China, purified by Protein-A affinity chromatography)
were in dried powder form, whereas human serum albumin was buffered with water. The band located at 981 cm−1in the human IgG spectrum
may arise from impurities.
Table 2. Normalized Spectra: Band Assignments and Statistical Comparisons between COVID-19 and Control Spectra in Band
Locations and Relative Absorbance
a
,
d
,
e
,
f
band locations/(cm−1) relative absorbance (a.u.)
assignments
19,27
control COVID-19 pcontrol COVID-19 p changes (%)
b
amide A 3285.65 (1.04) 3282.75 (1.19) *** 0.430 (0.028) 0.500 (0.049) *** +16.3
νas(CH3) 2958.31 (0.55) 2958.99 (0.83) *** 0.215 (0.007) 0.217 (0.008)
νas(CH2) 2930.76 (1.05) 2929.71 (1.55) *** 0.220 (0.010) 0.225 (0.015) *+2.3
νs(CH3) 2872.61 (0.49) 2873.14 (0.76) *** 0.150 (0.006) 0.151 (0.008)
amide I 1642.27 (2.28) 1636.84 (2.24) *** 1 (0)
c
1 (0)
c
amide II 1537.18 (1.05) 1538.19 (0.95) *** 0.878 (0.03) 0.857 (0.025) *** −2.4
δ(CH2) 1453.33 (0.62) 1452.9 (0.7) *** 0.342 (0.021) 0.339 (0.014)
νs(COO−) 1397.53 (1.05) 1398.22 (0.68) *** 0.386 (0.024) 0.380 (0.016)
amide III 1308.06 (1.7) 1311.19 (1.14) *** 0.280 (0.021) 0.279 (0.022)
νas(PO2−) 1242.29 (0.81) 1241.62 (1.01) *** 0.264 (0.023) 0.271 (0.016)
νs(C−O−C) 1170.12 (0.33) 1167.6 (2.85) *** 0.154 (0.016) 0.151 (0.010)
νs(PO2−) 1079.63 (0.77) 1077.36 (1.02) *** 0.162 (0.017) 0.193 (0.025) *** +19.1
a
νs: symmetric stretching vibrations; νas: asymmetric stretching vibrations; δ: bending vibrations. Data are in mean (standard deviation) or %.
b
Increases in average band absorbance compared to controls.
c
All the spectra were normalized to amide I.
d
Statistical differences were compared
between COVID-19 and the control group using one-way ANOVA.
e
*p< 0.05. **p< 0.01. ***p< 0.001.
f
See more in Figure S2.
Analytical Chemistry pubs.acs.org/ac Article
https://dx.doi.org/10.1021/acs.analchem.0c04049
Anal. Chem. 2021, 93, 2191−2199
2193

indicating the notable changes in protein and lipid
conformation. Bands related to proteins include amide A
(N−H stretching vibration), amide I (CO stretching),
amide II (C−N stretch and N−H bending), and amide III. As
seen from Table 2, the relative absorbance values of both
amide A and amide II showed significant differences between
COVID-19 and the control group (p< 0.001), denoting the
alterations in protein concentrations.
We found that the amide I band of the COVID-19 group
had a significant red shift of roughly 6 cm−1compared to the
control (p< 0.001, Figure 1a), suggesting the transitions in
protein secondary structures. This is further illustrated by the
SD-IR spectra (Figures 1b, S3, and Table S3). The band
centered at 1651 cm−1from the α-helix structure was
significantly lower (p< 0.001), while the band centered at
1632 cm−1attributed to the β-pleated sheet structure
26
was
significantly higher (p< 0.001) in the COVID-19 group,
responsible for the notable frequency shift of amide I in the
original spectra. Human serum albumin (HSA) (about 70% of
serum by weight), immunoglobulin (IgG: 14%), transferrin
(5.7%), and α-antitrypsin (0.7%) are among the most
abundant serum proteins (Table S2).
35
HSA is predominated
by α-helix and Ig by the β-sheet (also see Figure 1b).
36−38
Hence, bands centered at 1651 and 1632 cm−1principally
provide hints toward HSA and Ig in human sera, respectively
(see more in the Discussion section).
The peak at 1397 cm−1is associated with COO−symmetric
stretching mainly from aspartic acids and glutamic acids in
protein side chains.
34
No significant change was observed in
the absorbance of this band in the COVID-19 group, despite
the slight band shift. Bands centered at 1242 and 1078 cm−1
correspond to asymmetric and symmetric stretches from PO2−
groups, respectively.
34
Compared to controls, there was no
significant difference in the relative absorbance of the former
band in COVID-19 sample spectra (p> 0.05), whereas a
significant increase (by 19.1%) was found in the latter band (p
< 0.001, Figure 1a and Table 2). The PO2−functional group
may arise from nucleic acids or phospholipids in serum.
However, nucleic acids such as cell-free circulating DNA
(cirDNA)
21
or SARS-CoV-2 RNA
5
exhibit limited abundance
in human serum. Hence, increases in the band centered at
1078 cm−1can be attributed to phospholipids. It is known that
lecithin, sphingomyelin, and lysolecithin are the three major
phospholipids in normal human serum, accounting for 95% on
a weight percent basis (see also Table S1).
39,40
In the mild,
severe, and fatal COVID-19 patients, sphingolipids and
lysolecithin (12:0/0:0) were observed with a 3.5−5.5-fold
increase (log2fold change: 1.8−2.5), whereas lecithin
decreases by 50%−80% (−2.5 < log2fold change < −1.0).
41
Considering sphingolipids to be the most abundant phospho-
lipids in serum, the stronger absorption in the band centered at
1078 cm−1may reflect the higher total phospholipid content in
COVID-19 patients. This finding was further validated by the
SD-IR spectra shown in Figure 1b, where the νs(PO2−)
absorption band was found at 1086 and 1083 cm−1in the SD-
IR spectra of sphingomyelin and lysolecithin, respectively. This
slight band shift from 1079 cm−1may result from sample states
as the purchased samples were in dried powder form.
The bands centered at 1170 cm−1in the control group and
1166 cm−1in COVID-19 sample spectra originate from ester
C−O−C asymmetric stretching of phospholipids, triglycerides,
and cholesterol esters.
42
No significant absorbance alterations
Figure 2. Discrimination among normal controls and nonsevere and severe COVID-19 patients with unsupervised methods. (a,b) PCA score plot
using spectral ranges of 1600−1700 cm−1and 1000−1700 cm−1, respectively. Spectra from three patients are marked with arrows. NS = nonsevere.
S = severe. (c) Hierarchical cluster analysis (1000−1700 cm−1). The windows corresponding to the three groups are filled with different colors. 1 =
normal controls; 2 = nonsevere COVID-19 patients; 3 = severe COVID-19 patients.
Analytical Chemistry pubs.acs.org/ac Article
https://dx.doi.org/10.1021/acs.analchem.0c04049
Anal. Chem. 2021, 93, 2191−2199
2194

were observed. Nevertheless, we found that this band together
with the bands centered at 2925 and 2853 cm−1in the SD-IR
spectra had a significant blue shift in the absorption frequency
(Figure 1b and Table S3), suggesting the potential conforma-
tional changes in lipid components.
Differentiate COVID-19 Patients from Normal Con-
trols. PCA was first performed on the SD-IR spectra to reduce
the dimension of infrared spectral space. It requires the first
five and ten components to account for 90% of the total
variance in the data ranging from 1600 to 1700 cm−1and 1000
to 1700 cm−1, respectively. Nevertheless, the score plot with
respect to the first two PCs provides meaningful information
(Figure 2). Obvious transitions from normal controls to
nonsevere COVID-19 patients and to the severe ones were
observed (Figure 2a). Overlapping was mitigated when
applying the spectral range of 1000−1700 cm−1(Figure 2b).
This implies that other serum components in addition to
proteins may play a role in differentiating between healthy
controls and COVID-19 patients and evaluating the illness
severities. Two patients (NS348 and NS457) in the nonsevere
group and one patient (S55) in the severe group were found to
be closer to normal controls. It is noteworthy that patient
NS348 was in the early stage of illness with 2 days after onset,
while NS457 was an asymptomatic patient who was validated
with at least >14 d.a.o. S55 was a critically ill patient but with
time after onset of <7 days when sampled. Note that two
components explain about 60% and 55% of the total variance
when applying a spectral window of 1600−1700 or 1000−
1700 cm−1, respectively. For comparison with the 2-PC results,
the score plot regarding three PCs is shown in Figure S4.
We further applied HCA to classify the groups using the
shortest distance method and Euclidian distance based on the
spectral region of 1000−1700 cm−1. Most of the severe
patients were clustered together except six spectra from
patients S57, S14, S24, and S55 (Figure 2c). Among the
spectra from nonsevere patients, six of them from patients
NS348 and NS457 were mixed with normal controls, whereas
the remaining was separated well with others (Figure 2c).
Results of the two unsupervised methods show the potential
feasibility of FT-IR to discern COVID-19 patients in different
severities, yet supervised learning methods with the labeled
data set may provide more insightful information.
PLS-DA is commonly used in chemometrics for its ability to
handle multivariate data and high model interpretability.
31
We
first performed a PLS-DA analysis using SD-IR spectra in the
region between 1000 and 1700 cm−1. The first latent variable
(LV, i.e., score) was extracted under leave-one-out cross
validation with an R2Yvalue of 0.793 and a Q2value of 0.790.
Regularly, Q2> 0.5 is admitted for good model predictability.
43
The regression vector of variables (loadings for LV1) and the
corresponding variable importance in the projection (VIP)
values are presented in Figure 3a. VIP is a parameter to
evaluate the influence of individual X-variables on the model
and is preferred if the value is larger than one.
32,44
Clearly, the
most discriminatory bands are located at 1655, 1625, 1557,
1506, 1074, and 1035 cm−1. This finding is consistent with the
SD-IR spectra shown in Figure 1b.
In the PLS-DA model, normal controls and COVID-19
samples were labeled “0”and “1”, respectively. The receiver
operating characteristic (ROC) curve was generated by
disturbing the decision threshold. The area under the curve
(AUC) value of the model was 0.9947 (95% CI 0.9769−
0.9985). From the model output shown in Figure 3b, we
observed a distinct separation between the two classes, despite
six sample spectra which were from patient NS348 and NS457
mixed with controls, that is, when screening COVID-19
patients, especially the ones with moderate symptoms, such a
simple model based on a single latent variable may not be
effective enough. Nevertheless, SD-IR spectra coupled with
PLS-DA analysis achieve high sensitivity and specificity when
choosing proper decision thresholds, at least for the training
data in this work.
Differentiate COVID-19 from Normal Controls, Res-
piratory Viral Infections, and Inflammation-Related
Diseases. Patients with influenza A/influenza B/RSV
infections or common inflammatory diseases were enrolled in
as interferences to further assess the performance of the
proposed method. We first applied a database searching
algorithm based on Pearson’s correlation coefficient.
45
Despite
the high sensitivity for the controls, the overall performance is
limited (Table S4). PCA was not satisfactory either (not
shown), three PCs explaining about 50−55% of the data
variance when using different spectral windows. The above-
mentioned results highlight the risks of misclassification using
unsupervised methods.
Then, a triple-class PLS-DA model was established to
differentiate the following three groups: normal controls,
COVID-19, and other diseases. The spectral range between
900 and 1700 cm−1achieved the best results. Three most
frequent cross-validation (CV) methods, namely, leave-one-out
Figure 3. Results of the PLS-DA model for classification between COVID-19 and control groups. (a) Regression vector with respect to the spectral
region of 900−1700 cm−1. VIP values in different ranges are shown in different colors. VIP = variable influence on projection. (b) ROC plot of
COVID-19 samples (outer) and the model output (inner). Two threshold values corresponding to two points in the ROC curve were selected.
AUC = area under curve. CI = confidence interval.
Analytical Chemistry pubs.acs.org/ac Article
https://dx.doi.org/10.1021/acs.analchem.0c04049
Anal. Chem. 2021, 93, 2191−2199
2195

(LOO), 7-fold, and 10-fold were used to determine the
optimum number of latent variables by means of the prediction
error rate.
30,46
When applying 7-fold or 10-fold CV, the data
were randomly rearranged, which was repeated 100 times to
obtain statistical results. Clearly, three methods achieved
consistent prediction error rates, indicating good model
predictability (Figure 4a). In view of the model complexity,
finally, five LVs was selected. Fewer variables are not adequate,
whereas more variables contribute little to the prediction
capability.
The PLS-DA model with five LVs using the whole dataset
was then applied to produce the ROC curves (Figure 4b).
Here, the model output is a matrix of n×3, each column
corresponding to one class. By disturbing the decision
threshold, we got one ROC curve for each class. This helps
illustrate how one class is differentiated from others. As shown
in Figure 4b, the control group achieves the highest AUC value
of 0.9994 (95% CI: 0.9970−1.0000), which suggests that
normal controls are nearly not likely to be identified as patients
by the model and vice versa. The AUC values for COVID-19
and other diseases are 0.9561 (95% CI: 0.9071−0.9774) and
0.9588 (95% CI: 0.9327−0.9752), respectively. The sensitivity
and specificity of COVID-19 identification can be adjusted by
modifying the decision thresholds. For instance, when it was
set as 0.288 (threshold 1, Figure 4b), a high sensitivity of 87%
was achieved, whereas a high specificity of 98% was fulfilled
when it was set as 0.383 (threshold 2, Figure 4b, sensitivity:
83.1%). Overall, threshold 1 is more acceptable. We found that
nonsevere patients NS348 and NS457 were still mixed with
others (Figure 4b, inner graph). Finally, VIP scores were
calculated to inspect the most significant peaks for
classification. As indicated from Figure 4c, bands in spectral
ranges of 1450−1650 and 1050−1100 cm−1should be paid
attention to. Of a simple note, inflammatory markers may not
be likely to be distinguished by infrared spectroscopy because
of their relatively low levels (<1 mg/mL, or even lower than
ng/mL, Table S2). We may conclude that the discriminatory
bands may arise from subtle changes in proteins or lipids or
other constituents in serum.
■DISCUSSION
The selection of biospecimens is necessary to be discussed. In
this study, we chose serum because of its relatively stable
components, thus minimizing the individual differences caused
by disease-irrelevant factors. However, other types of speci-
mens also deserve trying, for example, white blood cells.
COVID-19 patients, especially for severe cases, are reported
with significantly lower total lymphocytes, CD4+T cells, CD8+
T cells, B cells, and NK cells (P < 0.001),
47
as well as reduced
percentages of monocytes, eosinophils, and basophils.
48,49
Virus-containing samples such as nasal and throat swabs may
be feasible too.
As for ATR−FT-IR measurements, several factors may
influence the qualities of the spectra. For instance, hemolysis
caused by improper sampling might lead to spectral distortion
and impede correct identification (data not shown). It is
known that unfolding, conformational changes, and denatura-
tion may happen in proteins when exposed to elevated
temperatures.
50,51
For the samples collected, preheating at 56
°C for 30 min before measurement contributes to minor
spectral differences in most cases, whereas it may lead to
relatively large variations in very few cases. The influence of the
thermal factor on serum components and the corresponding
infrared spectra is worthwhile to further investigate. Never-
Figure 4. PLS-DA model performances for the triple-class classification. (a) Prediction error rates as a function of the number of latent variables.
For 7-fold and 10-fold cross-validation, the error bars were presented. (b) ROC graphs for each group. The inner graph shows the model predicted
output of the COVID-19 class. The decision threshold values of 1 and 2 are 0.288 and 0.383, respectively. (c) VIP scores for each class. Significant
peaks were labeled.
Analytical Chemistry pubs.acs.org/ac Article
https://dx.doi.org/10.1021/acs.analchem.0c04049
Anal. Chem. 2021, 93, 2191−2199
2196

theless, the IgG secondary structure seems to be stable within
temperatures between 20 and 55 °C.
51
Of another note, when
considering the practical operation, protein coagulation
induced by heat may make it harder for uniform sampling.
Immune responses to infection can contribute to alterations
in serum constituents, and several biomarkers have been
suggested to identify certain infections. For example,
procalcitonin (PCT), circulating cytokines (interferon [IL]-
1β, IL-6, IL-18, etc.), and acute-phase proteins (C-reactive
protein [CRP], ferritin, etc.) have been used to differentiate
between bacterial and viral infection.
52,53
However, it is not
always an easy task to find specific markers for a given viral
infection. Over the past several months, proteomic and
metabolomic profiles of peripheral blood samples have been
investigated to correlate disease severity of COVID-19 with
certain proteins and lipids.
54,55
Details can be found elsewhere.
We know that FT-IR also provides hints toward proteins,
lipids, and other constituents in sera, but in a more
comprehensive and macroscopic view. Provided that human
serum is dominated by proteins and proteins are dominated by
albumin and Ig (Table S2, see also Figure 1b), we can
postulate that alterations in the amide I are closely related to
the two types of proteins. The increases in Ig levels and
decreases in albumin levels in the sera of patients with
COVID-19, revealed by the SD-IR spectra in Figure 1b, have
been proved by a lot of reports. The IgG level in the patient
sera elevates several times after the onset of COVID-19.
56
A
report by Long et al. showed that 19.5% (8/41) of the patients
with COVID-19 have a fourfold increase in the IgG titers.
9
The decreased albumin level was also reported by several
reports.
54,57
The reduction in the albumin level is related to the
acute phase response (APR) of patients to viral infection. The
hepatic synthesis of proteins is drastically regulated during the
acute phase of illnesses such as infection, tissue injury,
neoplastic growth, or immunological disorders, usually with
increased C-reactive protein (CRP) and serum amyloid A and
decreased transferrin and albumin.
58
The dysregulation of
phospholipids in patients with COVID-19 has also been
indicated by the SD-IR spectra shown in Figure 1b, which has
been discussed earlier (Results, part one).
More detailed interpretation of the infrared spectra and the
corresponding spectral differences still lack a lot of knowledge.
The multivariate and statistical analysis provided chances for
the evaluation of COVID-19 diagnosis by infrared spectrosco-
py. In summary, the second-derivative spectrum can increase
the separation of the overlapping bands and thus is more
powerful than the original spectrum. We show that both
unsupervised and supervised methods are promising to
differentiate COVID-19 patients from healthy controls.
Nonsevere and severe patients can be separated by
unsupervised methods such as PCA and HCA. However, as
indicated by this work, asymptomatic patients or newly
diagnosed patients with low IgG levels may not be correctly
identified. In this case, the decision threshold of PLS-DA
should be decided with caution. Lower threshold values yield
higher sensitivity but lower specificity.
For the purpose of diagnosis, which is no doubt tougher
than the only discrimination between COVID-19 and controls,
common respiratory viral infections or inflammation were
considered as interferences. Unsupervised methods were not
so effective, whereas the PLS-DA model yielded good
performances. For a given threshold, the model achieved a
sensitivity of 83.1% and specificity of 98%. Bands in spectral
ranges of 1450−1650 and 1050−1100 cm−1were the most
responsible for the model discrimination capability. Further
illustration is required in future work.
■CONCLUSIONS
Taking the time cost, operation complexity, and detection
performance into consideration, FTIR coupled with multi-
variate analysis is a feasible tool for screening and primary
diagnosis of COVID-19. Several serum constituents including,
but not just, antibodies and serum lipids could be reflected on
the infrared spectra, serving as “chemical fingerprints”and
accounting for good model performances. It was also showed
that this assay exhibited high identification specificity for
COVID-19 under the intervention from common respiratory
viruses and inflammation-related diseases.
In terms of practical use, more clinical data are required to
generalize the model, and special attention should be paid to
the asymptomatic patients. Nevertheless, infrared spectroscopy
can serve as an assistant diagnosis tool as a supplement to in-
use techniques. The feasibility of other common specimens
such as oropharyngeal swabs, identification and interpretation
of the spectral markers, the potentials of FTIR spectroscopy on
antibody response monitoring, and COVID-19 severity
prediction require further investigations.
■ASSOCIATED CONTENT
*
sıSupporting Information
The Supporting Information is available free of charge at
https://pubs.acs.org/doi/10.1021/acs.analchem.0c04049.
Patients with inflammatory diseases included in this
study; molar and mass concentrations of major
constituents in normal human serum/plasma; second-
derivative spectra: band assignments and statistical
comparisons between COVID-19 and control serum in
band locations and relative absorbance; performance of
the database searching algorithm using different spectral
regions; original and the corresponding second-deriva-
tive infrared spectra from healthy controls (number of
spectra = 51) and nonsevere (n= 18) and severe (n=
59) COVID-19 patients; box plot of relative absorbance
(to amide I) of the existing bands in the original spectra;
box plot of absorbance of the existing bands in the SD-
IR spectra; and three-principal-component score plot of
PCA results (PDF)
■AUTHOR INFORMATION
Corresponding Authors
Haiyun Luo −Department of Electrical Engineering, Tsinghua
University, Beijing 100084, China; Email: lhy@
tsinghua.edu.cn
Qun Zhou −Department of Chemistry, Key Laboratory of
Bioorganic Phosphorus Chemistry & Chemical Biology
(Ministry of Education), Tsinghua University, Beijing
100084, China; Email: zhouqun@tsinghua.edu.cn
Yingchun Xu −Department of Laboratory Medicine, Peking
Union Medical College Hospital, Peking Union Medical
College, Chinese Academy of Medical Sciences, Beijing
100730, China; Email: xycpumch@139.com
Analytical Chemistry pubs.acs.org/ac Article
https://dx.doi.org/10.1021/acs.analchem.0c04049
Anal. Chem. 2021, 93, 2191−2199
2197

Authors
Liyang Zhang −Department of Electrical Engineering,
Tsinghua University, Beijing 100084, China; orcid.org/
0000-0001-9909-9263
Meng Xiao −Department of Laboratory Medicine, Peking
Union Medical College Hospital, Peking Union Medical
College, Chinese Academy of Medical Sciences, Beijing
100730, China
Yao Wang −Department of Laboratory Medicine, Peking
Union Medical College Hospital, Peking Union Medical
College, Chinese Academy of Medical Sciences, Beijing
100730, China
Siqi Peng −Department of Electrical Engineering, Tsinghua
University, Beijing 100084, China; orcid.org/0000-0001-
5418-3656
Yu Chen −Department of Laboratory Medicine, Peking Union
Medical College Hospital, Peking Union Medical College,
Chinese Academy of Medical Sciences, Beijing 100730, China
Dong Zhang −Department of Laboratory Medicine, Peking
Union Medical College Hospital, Peking Union Medical
College, Chinese Academy of Medical Sciences, Beijing
100730, China
Dongheyu Zhang −Department of Electrical Engineering,
Tsinghua University, Beijing 100084, China
Yuntao Guo −Department of Electrical Engineering, Tsinghua
University, Beijing 100084, China
Xinxin Wang −Department of Electrical Engineering,
Tsinghua University, Beijing 100084, China
Complete contact information is available at:
https://pubs.acs.org/10.1021/acs.analchem.0c04049
Author Contributions
∥
L.Z., M.X., and Y.W. contributed equally.
Notes
The authors declare no competing financial interest.
■ACKNOWLEDGMENTS
This work was funded by the National Natural Science
Foundation of China (52041701), the Tsinghua University-
Peking Union Medical College Hospital Initiative Scientific
Research Program (20191080604), Tsinghua University
Spring Breeze Fund (2020Z99CFG007), and Beijing Nova
Program (Z201100006820127).
■REFERENCES
(1) WHO, Coronavirus disease (COVID-2019) situation reports.
https://www.who.int/emergencies/diseases/novel-coronavirus-2019/
situation-reports/ (accessed June 1, 2020), June 1, 2020.
(2) Zhou, P.; Yang, X.-L.; Wang, X.-G.; et al. Nature 2020,579,
270−273.
(3) Yang, Y.; Yang, M.; Yuan, J.; Wang, F.; Wang, Z.; Li, J.; Zhang,
M.; Xing, L.; Wei, J.; Peng, L.; Wong, G.; Zheng, H.; Wu, W.; Shen,
C.; Liao, M.; Feng, K.; Li, J.; Yang, Q.; Zhao, J.; Liu, L.; et al. The
Innovation 2020,1, No. 100061.
(4) Ai, T.; Yang, Z.; Hou, H.; et al. Radiology 2020,296, E32−E40.
(5) Wölfel, R.; Corman, V. M.; Guggemos, W.; et al. Nature 2020,
581, 465−469.
(6) Young, B. E.; Ong, S. W. X.; Kalimuddin, S.; et al. JAMA, J. Am.
Med. Assoc. 2020,323, 1488−1494.
(7) Huang, C.; Wang, Y.; Li, X.; et al. Lancet 2020,395, 497−506.
(8) Li, X.; Geng, M.; Peng, Y.; Meng, L.; Lu, S. J. Pharm. Anal. 2020,
10, 102−108.
(9) Long, Q.-X.; Liu, B.-Z.; Deng, H.-J.; et al. Nat. Med. 2020,26,
845−848.
(10) Guo, L.; Ren, L.; Yang, S.; et al. Clin. Infect. Dis. 2020,71, 778−
785.
(11) Li, Z.; Yi, Y.; Luo, X.; et al. J. Med. Virol. 2020,92, 1518−1524.
(12) Zhao, J.; Yuan, Q.; Wang, H.; et al. Clin. Infect. Dis. 2020,71,
2027.
(13) Santos, M. C. D.; Nascimento, Y. M.; Araújo, J. M. G.; Lima, K.
M. G. RSC Adv. 2017,7, 25640−25649.
(14) Roy, S.; Perez-Guaita, D.; Bowden, S.; Heraud, P.; Wood, B. R.
Clinical Spectroscopy 2019,1, 100001.
(15) Sitole, L.; Steffens, F.; Krüger, T. P. J.; Meyer, D. OMICS 2014,
18, 513−523.
(16) Sakudo, A.; Tsenkova, R.; Onozuka, T.; et al. Microbiol.
Immunol. 2005,49, 695−701.
(17) Salman, A.; Erukhimovitch, V.; Talyshinsky, M.; Huleihil, M.;
Huleihel, M. Biopolymers 2002,67, 406−412.
(18) Erukhimovitch, V.; Talyshinsky, M.; Souprun, Y.; Huleihel, M.
FTIR Microscopy Detection of Cells Infected With Viruses. In DNA
Viruses: Methods and Protocols; Lieberman, P. M., Ed.; Humana Press:
Totowa, NJ, 2005; Vol. 292, p 161.
(19) Santos, M. C. D.; Nascimento, Y. M.; Monteiro, J. D.; et al.
Anal. Methods 2018,10, 1280−1285.
(20) Agbaria, A. H.; Beck Rosen, G.; Lapidot, I.; et al. Anal. Chem.
2018,90, 7888−7895.
(21) Eccles, R. Lancet Infect. Dis. 2005,5, 718−725.
(22) Falsey, A. R.; Walsh, E. E. Clin. Microbiol. Rev. 2000,13, 371−
384.
(23) Guan, W. J.; Ni, Z. Y.; Hu, Y.; et al. N. Engl. J. Med. 2020,382,
1708−1720.
(24) China National Guidelines for Diagnosis and Treatment of
Corona Virus Disease 2019 (COVID-19). http://www.nhc.gov.cn/
yzygj/s7653p/202002/d4b895337e19445f8d728fcaf1e3e13a.shtml
(accessed Feb 15, 2020), Feb 8, 2020.
(25) Zhang, X.; Guo, Y.; Song, Y.; et al. Proteomics 2006,6, 5260−
5268.
(26) Agbaria, A. H.; Beck Rosen, G.; Lapidot, I.; et al. Anal. Chem.
2018,90, 7888−7895.
(27) Wenning, M.; Scherer, S. Appl. Microbiol. Biotechnol. 2013,97,
7111−7120.
(28) Zarnowiec, P.; Lechowicz, L.; Czerwonka, G.; Kaca, W. Curr.
Med. Chem. 2015,22, 1710−1718.
(29) Carneiro, C. R.; Silva, C. S.; de Carvalho, M. A.; Pimentel, M.
F.; Talhavini, M.; Weber, I. T. Anal. Chem. 2019,91, 12444−12452.
(30) Lee, L. C.; Liong, C.-Y.; Jemain, A. A. Analyst 2018,143,
3526−3539.
(31) Gromski, P. S.; Muhamadali, H.; Ellis, D. I.; et al. Anal. Chim.
Acta 2015,879,10−23.
(32) Mehmood, T.; Liland, K. H.; Snipen, L.; Sæbø, S. Chemom.
Intell. Lab. Syst. 2012,118,62−69.
(33) Dé
lé
ris, G.; Petibois, C. Vib. Spectrosc. 2003,32, 129−136.
(34) Naumann, D. Appl. Spectrosc. Rev. 2001,36, 239−298.
(35) Anderson, N. L.; Anderson, N. G. Mol. Cell. Proteomics 2002,1,
845−867.
(36) He, X. M.; Carter, D. C. Nature 1992,358, 209−215.
(37) Buijs, J.; Norde, W.; Lichtenbelt, J. W. T. Langmuir 1996,12,
1605−1613.
(38) Schroeder, H. W.; Cavacini, L. J. Allergy Clin. Immunol. 2010,
125, S41−S52.
(39) Williams, J. H.; Kuchmak, M.; Witter, R. F. Lipids 1966,1,89−
97.
(40) Hidaka, H.; Yamauchi, K.; Ohta, H.; Akamatsu, T.; Honda, T.;
Katsuyama, T. Clin. Biochem. 2008,41, 1211−1217.
(41) Wu, D.; Shu, T.; Yang, X.; et al. Natl. Sci. Rev. 2020,7, 1157−
1168.
(42) Liu, K.-Z.; Shaw, R. A.; Man, A.; Dembinski, T. C.; Mantsch, H.
H. Clin. Chem. 2002,48, 499−506.
(43) Triba, M. N.; Le Moyec, L.; Amathieu, R.; et al. Mol. BioSyst.
2015,11,13−19.
(44) Galindo-Prieto, B.; Eriksson, L.; Trygg, J. J. Chemom. 2014,28,
623−632.
Analytical Chemistry pubs.acs.org/ac Article
https://dx.doi.org/10.1021/acs.analchem.0c04049
Anal. Chem. 2021, 93, 2191−2199
2198

(45) Primpke, S.; Lorenz, C.; Rascher-Friesenhausen, R.; Gerdts, G.
Anal. Methods 2017,9, 1499−1511.
(46) Liu, W.; Sun, Z.; Chen, J.; Jing, C. J. Spectrosc. 2016,2016,
1603609.
(47) Wang, F.; Nie, J.; Wang, H.; et al. J. Infect. Dis. 2020,221,
1762−1769.
(48) Qin, C.; Zhou, L.; Hu, Z.; et al. Clin. Infect. Dis. 2020,71, 762−
768.
(49) Li, Q.; Ding, X.; Xia, G.; et al. EClinicalMedicine 2020,23,
100375.
(50) Huggins, C.; Jensen, E. V. J. Biol. Chem. 1949,179, 645−654.
(51) Vermeer, A. W. P.; Norde, W. Biophys. J. 2000,78, 394−404.
(52) Chalupa, P.; Beran, O.; Herwald, H.; Kaspř
íková
, N.; Holub, M.
Infection 2011,39, 411−417.
(53) Slaats, J.; ten Oever, J.; van de Veerdonk, F. L.; Netea, M. G.
PLoS Pathog. 2016,12, e1005973.
(54) Shen, B.; Yi, X.; Sun, Y.; et al. Cell 2020,182,59−72.
(55) Whetton, A. D.; Preston, G. W.; Abubeker, S.; Geifman, N. J.
Proteome Res. 2020,19, 4219−4232.
(56) Zhang, B.; Zhou, X.; Zhu, C.; et al. Front. Mol. Biosci. 2020,7,
157.
(57) Chen, N.; Zhou, M.; Dong, X.; et al. Lancet 2020,395, 507−
513.
(58) Heinrich, P. C.; Castell, J. V.; Andus, T. Biochem. J. 1990,265,
621−636.
Analytical Chemistry pubs.acs.org/ac Article
https://dx.doi.org/10.1021/acs.analchem.0c04049
Anal. Chem. 2021, 93, 2191−2199
2199