Rapid diagnosis of COVID-19 using FT-IR ATR spectroscopy and machine learning

Infrared spectroscopy for fast screening of diabetes and periodontitis

SIGNIFICANCE

FT-IR is an important and emerging tool, providing information related to the biochemical composition of biofluids. It is important to demonstrate that there is an efficacy in separating healthy and diseased groups, helping to establish FT-IR uses as fast screening tool.

AIM

Via saliva diagnosis evaluate the accuracy of FT-IR associate with machine learning model for classification among healthy (control group), diabetic (D) and periodontitis (P) patients and the association of both diseases (DP).

APPROACH

Eighty patients diagnosed with diabetes and periodontitis through conventional methods were recruited and allocated in one of the four groups. Saliva samples were collected from participants of each group (n = 20) and were processed using Bruker Alpha II spectrometer in a FT-IR spectral fingerprint region between 600 and-1800 cm-1, followed by data preprocessing and analysis using machine learning tools.

RESULTS

Various FTI-R peaks were detectable and attributed to specific vibrational modes, which were classified based on confusion matrices showed in paired groups. The highest true positive rates (TPR) appeared between groups C vs D (93.5 % ± 2.7 %), groups C vs. DP (89.2 % ± 4.1 %), and groups D and P (90.4 % ± 3.2 %). However, P vs DP presented higher TPR for DP (84.1 % ±3.1 %) while D vs. DP the highest rate for DP was 81.7 % ± 4.3 %. Analyzing all groups together, the TPR decreased.

CONCLUSION

The system used is portable and robust and can be widely used in clinical environments and hospitals as a new diagnostic technique. Studies in our groups are being conducted to solidify and expand data analysis methods with friendly language for healthcare professionals. It was possible to classify healthy patients in a range of 78-93 % of accuracy. Range over 80 % of accuracy between periodontitis and diabetes were observed. A general classification model with lower TPR instead of a pairwise classification would only have advantages in scenarios where no prior patient information is available regarding diabetes and periodontitis status.

Two-trace two-dimensional correlation spectra (2T2D-COS) analysis using FTIR spectra to monitor the immune response by COVID-19

There is a growing trend in using saliva for SARS-CoV-2 detection with reasonable accuracy. We have studied the responses of IgA, IgG, and IgM in human saliva by directly comparing disease with control analyzing two-trace two-dimensional correlation spectra (2T2D-COS) employing Fourier transform infrared (FTIR) spectra. It explores the molecular-level variation between control and COVID-19 saliva samples. The advantage of 2T2D spectra is that it helps in discriminating remarkably subtle features between two simple pairs of spectra. It gives spectral information from highly overlapped bands associated with different systems. The clinical findings from 2T2D show the decrease of IgG and IgM salivary antibodies in the 50, 60, 65, and 75-years COVID-19 samples. Among the various COVID-19 populations studied the female 30-years group reveals defense mechanisms exhibited by IgM and IgA. Lipids and fatty acids decrease, resulting in lipid oxidation due to the SARS-CoV-2 in the samples studied. Study shows salivary thiocyanate plays defense against SARS-CoV-2 in the male population in 25 and 35 age groups. The receiver operation characteristics statistical method shows a sensitivity of 98% and a specificity of 94% for the samples studied. The measure of accuracy computed as F score and G score has a high value, supporting our study's validation. Thus, 2T2D-COS analysis can potentially monitor the progression of immunoglobulin's response function to COVID-19 with reasonable accuracy, which could help diagnose clinical trials. KEY MESSAGES: The molecular profile of salivary antibodies is well resolved and identified from 2T2D-COS FTIR spectra. The IgG antibody plays a significant role in the defense mechanism against SARS-CoV-2 in 25-40 years. 2T2D-COS reveals the absence of salivary thiocyanate in the 40-75 years COVID-19 population. The receiver operation characteristic (ROC) analysis validates our study with high sensitivity and specificity.

Effect of pre-analytical variables on Raman and FTIR spectral content of lymphocytes

The use of Fourier transform infrared (FTIR) and Raman spectroscopy (RS) for the analysis of lymphocytes in clinical applications is increasing in the field of biomedicine. The pre-analytical phase, which is the most vulnerable stage of the testing process, is where most errors and sample variance occur; however, it is unclear how pre-analytical variables affect the FTIR and Raman spectra of lymphocytes. In this study, we evaluated how pre-analytical procedures undertaken before spectroscopic analysis influence the spectral integrity of lymphocytes purified from the peripheral blood of male volunteers (n = 3). Pre-analytical variables investigated were associated with (i) sample preparation, (blood collection systems, anticoagulant, needle gauges), (ii) sample storage (fresh or frozen), and (iii) sample processing (inter-operator variability, time to lymphocyte isolation). Although many of these procedural pre-analytical variables did not alter the spectral signature of the lymphocytes, evidence of spectral effects due to the freeze-thaw cycle, in vitro culture inter-operator variability and the time to lymphocyte isolation was observed. Although FTIR and RS possess clinical potential, their translation into a clinical environment is impeded by a lack of standardisation and harmonisation of protocols related to the preparation, storage, and processing of samples, which hinders uniform, accurate, and reproducible analysis. Therefore, further development of protocols is required to successfully integrate these techniques into current clinical workflows.

Fourier-transform infrared spectroscopy: a universal optical sensing technique with auspicious application prospects in the diagnosis and management of autoimmune diseases

Autoimmune diseases (AIDs) are poorly understood clinical syndromes due to breakdown of immune tolerance towards specific types of self-antigens. They are generally associated with an inflammatory response mediated by lymphocytes T, autoantibodies or both. Ultimately, chronic inflammation culminates in tissue damages and clinical manifestations. AIDs affect 5% of the world population, and they represent the main cause of fatality in young to middle-aged females. In addition, the chronic nature of AIDs has a devastating impact on the patient's quality of life. It also places a heavy burden on the health care system. Establishing a rapid and accurate diagnosis is considered vital for an ideal medical management of these autoimmune disorders. However, for some AIDs, this task might be challenging. Vibrational spectroscopies, and more particularly Fourier-transform infrared (FTIR) spectroscopy, have emerged as universal analytical techniques with promising applications in the diagnosis of various types of malignancies and metabolic and infectious diseases. The high sensitivity of these optical sensing techniques and their minimal requirements for test reagents qualify them to be ideal analytical techniques. The aim of the current review is to explore the potential applications of FTIR spectroscopy in the diagnosis and management of most common AIDs. It also aims to demonstrate how this technique has contributed to deciphering the biochemical and physiopathological aspects of these chronic inflammatory diseases. The advantages that can be offered by this optical sensing technique over the traditional and gold standard methods used in the diagnosis of these autoimmune disorders have also been extensively discussed.

Surface-Enhanced Raman Analysis of Uric Acid and Hypoxanthine Analysis in Fractionated Bodily Fluids

In recent years, the disease burden of hyperuricemia has been increasing, especially in high-income countries and the economically developing world with a Western lifestyle. Abnormal levels of uric acid and hypoxanthine are associated with many diseases, and therefore, to demonstrate improved methods of uric acid and hypoxanthine detection, three different bodily fluids were analysed using surface-enhanced Raman spectroscopy (SERS) and high-performance liquid chromatography (HPLC). Gold nanostar suspensions were mixed with series dilutions of uric acid and hypoxanthine, 3 kDa centrifugally filtered human blood serum, urine and saliva. The results show that gold nanostars enable the quantitative detection of the concentration of uric acid and hypoxanthine in the range 5-50 μg/mL and 50-250 ng/mL, respectively. The peak areas of HPLC and maximum peak intensity of SERS have strongly correlated, notably with the peaks of uric acid and hypoxanthine at 1000 and 640 cm^-1, respectively. The r² is 0.975 and 0.959 for uric acid and hypoxanthine, respectively. Each of the three body fluids has a number of spectral features in common with uric acid and hypoxanthine. The large overlap of the spectral bands of the SERS of uric acid against three body fluids at spectra peaks were at 442, 712, 802, 1000, 1086, 1206, 1343, 1436 and 1560 cm^-1. The features at 560, 640, 803, 1206, 1290 and 1620 cm^-1 from hypoxanthine were common to serum, saliva and urine. There is no statistical difference between HPLC and SERS for determination of the concentration of uric acid and hypoxanthine (p > 0.05). For clinical applications, 3 kDa centrifugal filtration followed by SERS can be used for uric acid and hypoxanthine screening is, which can be used to reveal the subtle abnormalities enhancing the great potential of vibrational spectroscopy as an analytical tool. Our work supports the hypnosis that it is possible to obtain the specific concentration of uric acid and hypoxanthine by comparing the SER signals of serum, saliva and urine. In the future, the analysis of other biofluids can be employed to detect biomarkers for the diagnosis of systemic pathologies.

ATR-FTIR spectroscopy and chemometrics as a quick and simple alternative for discrimination of SARS-CoV-2 infected food of animal origin

Alternative routes such as virus transmission or cross-contamination by food have been suggested, due to reported cases of SARS-CoV-2 in frozen chicken wings and fish or seafood. Delay in routine testing due to the dependence on the PCR technique as the standard method leads to greater virus dissemination. Therefore, alternative detection methods such as FTIR spectroscopy emerge as an option. Here, we demonstrate a fast (3 min), simple and reagent-free methodology using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for discrimination of food (chicken, beef and fish) contaminated with the SARS-CoV-2 virus. From the IR spectra of the samples, the "bio-fingerprint" (800 - 1900 cm^-1) was selected to investigate the distinctions caused by the virus contamination. Exploratory analysis of the spectra, using Principal Component of Analysis (PCA), indicated the differentiation in the data due to the presence of single bands, marked as contamination from nucleic acids including viral RNA. Furthermore, the partial least squares discriminant analysis (PLS-DA) classification model allowed for discrimination of each matrix in its pure form and its contaminated counterpart with sensitivity, specificity and accuracy of 100 %. Therefore, this study indicates that the use of ATR-FTIR can offer a fast and low cost and not require chemical reagents and with minimal sample preparation to detect the SARS-CoV-2 virus in food matrices, ensuring food safety and non-dissemination by consumers.

Robustness of FTIR-Based Ultrarapid COVID-19 Diagnosis Using PLS-DA

The World Health Organization (WHO) declared the Omicron variant (B.1.1.529) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen responsible for the Coronavirus disease 2019 (COVID-19) pandemic, as a variant of concern on 26 November 2021. By this time, 42% of the world's population had received at least one dose of the vaccine against COVID-19. As on 1 October 2022, only 68% of the world population got the first dose of the vaccine. Although the vaccination is incredibly protective against severe complications of the disease and death, the highly contagious Omicron variant, compared to the Delta variant (B.1.617.2), has led the whole world into more chaotic situations. Furthermore, the virus has a high mutation rate, and hence, the possibility of a new variant of concern in the future cannot be ruled out. To face such a challenging situation, paramount importance should be given to rapid diagnosis and isolation of the infected patient. Current diagnosis methods, including reverse transcription-polymerase chain reaction and rapid antigen tests, face significant burdens during a COVID-19 wave. However, studies reported ultrarapid, reagent-free, cost-efficient, and non-destructive diagnosis methods based on chemometrics for COVID-19 and COVID-19 severity diagnosis. These studies used a smaller sample cohort to construct the diagnosis model and failed to discuss the robustness of the model. The current study systematically evaluated the robustness of the diagnosis models trained using smaller (real and augmented spectra) and larger (augmented spectra) datasets. The Monte Carlo cross-validation and permutation test results suggest that diagnosis using models trained by larger datasets was accurate and statistically significant (Q ² > 99% and AUROC = 100%).

Contributions of vibrational spectroscopy to virology: A review

Vibrational spectroscopic techniques, both infrared absorption and Raman scattering, are high precision, label free analytical techniques which have found applications in fields as diverse as analytical chemistry, pharmacology, forensics and archeometrics and, in recent times, have attracted increasing attention for biomedical applications. As analytical techniques, they have been applied to the characterisation of viruses as early as the 1970 s, and, in the context of the coronavirus disease 2019 (COVID-19) pandemic, have been explored in response to the World Health Organisation as novel methodologies to aid in the global efforts to implement and improve rapid screening of viral infection. This review considers the history of the application of vibrational spectroscopic techniques to the characterisation of the morphology and chemical compositions of viruses, their attachment to, uptake by and replication in cells, and their potential for the detection of viruses in population screening, and in infection response monitoring applications. Particular consideration is devoted to recent efforts in the detection of severe acute respiratory syndrome coronavirus 2, and monitoring COVID-19.

Diagnostic performance of attenuated total reflection Fourier-transform infrared spectroscopy for detecting COVID-19 from routine nasopharyngeal swab samples

Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with machine learning-based partial least squares discriminant analysis (PLS-DA) was applied to study if severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could be detected from nasopharyngeal swab samples originally collected for polymerase chain reaction (PCR) analysis. Our retrospective study included 558 positive and 558 negative samples collected from Northern Finland. Overall, we found moderate diagnostic performance for ATR-FTIR when PCR analysis was used as the gold standard: the average area under the receiver operating characteristics curve (AUROC) was 0.67-0.68 (min. 0.65, max. 0.69) with 20, 10 and 5 k-fold cross validations. Mean accuracy, sensitivity and specificity was 0.62-0.63 (min. 0.60, max. 0.65), 0.61 (min. 0.58, max. 0.65) and 0.64 (min. 0.59, max. 0.67) with 20, 10 and 5 k-fold cross validations. As a conclusion, our study with relatively large sample set clearly indicate that measured ATR-FTIR spectrum contains specific information for SARS-CoV-2 infection (P < 0.001 for AUROC in label permutation test). However, the diagnostic performance of ATR-FTIR remained only moderate, potentially due to low concentration of viral particles in the transport medium. Further studies are needed before ATR-FTIR can be recommended for fast screening of SARS-CoV-2 from nasopharyngeal swab samples.

Insights into Biochemical Sources and Diffuse Reflectance Spectral Features for Colorectal Cancer Detection and Localization

Colorectal cancer (CRC) is the third most common and second most deadly type of cancer worldwide. Early detection not only reduces mortality but also improves patient prognosis by allowing the use of minimally invasive techniques to remove cancer while avoiding major surgery. Expanding the use of microsurgical techniques requires accurate diagnosis and delineation of the tumor margins in order to allow complete excision of cancer. We have used diffuse reflectance spectroscopy (DRS) to identify the main optical CRC biomarkers and to optimize parameters for the integration of such technologies into medical devices. A total number of 2889 diffuse reflectance spectra were collected in ex vivo specimens from 47 patients. Short source-detector distance (SDD) and long-SDD fiber-optic probes were employed to measure tissue layers from 0.5 to 1 mm and from 0.5 to 1.9 mm deep, respectively. The most important biomolecules contributing to differentiating DRS between tissue types were oxy- and deoxy-hemoglobin (Hb and HbO₂), followed by water and lipid. Accurate tissue classification and potential DRS device miniaturization using Hb, HbO₂, lipid and water data were achieved particularly well within the wavelength ranges 350-590 nm and 600-1230 nm for the short-SDD probe, and 380-400 nm, 420-610 nm, and 650-950 nm for the long-SDD probe.

Integrated System for On-Site Rapid and Safe Screening of COVID-19

Since the outbreak of coronavirus disease 2019 (COVID-19), the epidemic has been spreading around the world for more than 2 years. Rapid, safe, and on-site detection methods of COVID-19 are in urgent demand for the control of the epidemic. Here, we established an integrated system, which incorporates a machine-learning-based Fourier transform infrared spectroscopy technique for rapid COVID-19 screening and air-plasma-based disinfection modules to prevent potential secondary infections. A partial least-squares discrimination analysis and a convolutional neural network model were built using the collected infrared spectral dataset containing 857 training serum samples. Furthermore, the sensitivity, specificity, and prediction accuracy could all reach over 94% from the results of the field test regarding 968 blind testing samples. Additionally, the disinfection modules achieved an inactivation efficiency of 99.9% for surface and airborne tested bacteria. The proposed system is conducive and promising for point-of-care and on-site COVID-19 screening in the mass population.

Machine Learning Enhanced Optical Spectroscopy for Disease Detection

Optical spectroscopy plays an important role in disease detection. Improving the sensitivity and specificity of spectral detection has great importance in the development of accurate diagnosis. The development of artificial intelligence technology provides a great opportunity to improve the detection accuracy through machine learning methods. In this Perspective, we focus on the combination of machine learning methods with the optical spectroscopy methods widely used for disease detection, including absorbance, fluorescence, scattering, FTIR, terahertz, etc. By comparing the spectral analysis with different machine learning methods, we illustrate that the support vector machine and convolutional neural network are most effective, which have potential to further improve the classification accuracy to distinguish disease subtypes if these machine learning methods are used. This Perspective broadens the scope of optical spectroscopy enhanced by machine learning and will be useful for the development of disease detection.

Potential of ATR-FTIR-Chemometrics in Covid-19: Disease Recognition

The COVID-19 pandemic has caused major disturbances to human health and economy on a global scale. Although vaccination campaigns and important advances in treatments have been developed, an early diagnosis is still crucial. While PCR is the golden standard for diagnosing SARS-CoV-2 infection, rapid and low-cost techniques such as ATR-FTIR followed by multivariate analyses, where dimensions are reduced for obtaining valuable information from highly complex data sets, have been investigated. Most dimensionality reduction techniques attempt to discriminate and create new combinations of attributes prior to the classification stage; thus, the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. In this work, we developed a method for evaluating SARS-CoV-2 infection and COVID-19 disease severity on infrared spectra of sera, based on a rather simple feature selection technique (correlation-based feature subset selection). Dengue infection was also evaluated for assessing whether selectivity toward a different virus was possible with the same algorithm, although independent models were built for both viruses. High sensitivity (94.55%) and high specificity (98.44%) were obtained for assessing SARS-CoV-2 infection with our model; for severe COVID-19 disease classification, sensitivity is 70.97% and specificity is 94.95%; for mild disease classification, sensitivity is 33.33% and specificity is 94.64%; and for dengue infection assessment, sensitivity is 84.27% and specificity is 94.64%.

Diagnostic Tools for Rapid Screening and Detection of SARS-CoV-2 Infection

The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has severely impacted human health and the health management system globally. The ongoing pandemic has required the development of more effective diagnostic strategies for restricting deadly disease. For appropriate disease management, accurate and rapid screening and isolation of the affected population is an efficient means of containment and the decimation of the disease. Therefore, considerable efforts are being directed toward the development of rapid and robust diagnostic techniques for respiratory infections, including SARS-CoV-2. In this article, we have summarized the origin, transmission, and various diagnostic techniques utilized for the detection of the SARS-CoV-2 virus. These higher-end techniques can also detect the virus copy number in asymptomatic samples. Furthermore, emerging rapid, cost-effective, and point-of-care diagnostic devices capable of large-scale population screening for COVID-19 are discussed. Finally, some breakthrough developments based on spectroscopic diagnosis that could revolutionize the field of rapid diagnosis are discussed.

Development of novel spectroscopic and machine learning methods for the measurement of periodic changes in COVID-19 antibody level

In this research, blood samples of 47 patients infected by COVID were analyzed. The samples were taken on the 1st, 3rd and 6th month after the detection of COVID infection. Total antibody levels were measured against the SARS-CoV-2 N antigen and surrogate virus neutralization by serological methods. To differentiate COVID patients with different antibody levels, Fourier Transform InfraRed (FTIR) and Raman spectroscopy methods were used. The spectroscopy data were analyzed by multivariate analysis, machine learning and neural network methods. It was shown, that analysis of serum using the above-mentioned spectroscopy methods allows to differentiate antibody levels between 1 and 6 months via spectral biomarkers of amides II and I. Moreover, multivariate analysis showed, that using Raman spectroscopy in the range between 1317 cm^-1 and 1432 cm^-1, 2840 cm^-1 and 2956 cm^-1 it is possible to distinguish patients after 1, 3, and 6 months from COVID with a sensitivity close to 100%.

Near-Infrared Spectroscopy as a Potential COVID-19 Early Detection Method: A Review and Future Perspective

The coronavirus disease 2019 (COVID-19) pandemic is a worldwide health anxiety. The rapid dispersion of the infection globally results in unparalleled economic, social, and health impacts. The pathogen that causes COVID-19 is known as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A fast and low-cost diagnosis method for COVID-19 disease can play an important role in controlling its proliferation. Near-infrared spectroscopy (NIRS) is a quick, non-destructive, non-invasive, and inexpensive technique for profiling the chemical and physical structures of a wide range of samples. Furthermore, the NIRS has the advantage of incorporating the internet of things (IoT) application for the effective control and treatment of the disease. In recent years, a significant advancement in instrumentation and spectral analysis methods has resulted in a remarkable impact on the NIRS applications, especially in the medical discipline. To date, NIRS has been applied as a technique for detecting various viruses including zika (ZIKV), chikungunya (CHIKV), influenza, hepatitis C, dengue (DENV), and human immunodeficiency (HIV). This review aims to outline some historical and contemporary applications of NIRS in virology and its merit as a novel diagnostic technique for SARS-CoV-2.

Micro-Fourier-transform infrared reflectance spectroscopy as tool for probing IgG glycosylation in COVID-19 patients

It has been reported that patients diagnosed with COVID-19 become critically ill primarily around the time of activation of the adaptive immune response. However the role of antibodies in the worsening of disease is not obvious. Higher titers of anti-spike immunoglobulin IgG1 associated with low fucosylation of the antibody Fc tail have been associated to excessive inflammatory response. In contrast it has been also reported that NP-, S-, RBD- specific IgA, IgG, and IgM are not associated with SARS-CoV-2 viral load, indicating that there is no obvious correlation between antibody response and viral antigen detection. In the present work the micro-Fourier-transform infrared reflectance spectroscopy (micro-FTIR) was employed to investigate blood serum samples of healthy and COVID-19-ill (mild or oligosymptomatic) individuals (82 healthcare workers volunteers in “Instituto de Infectologia Emilio Ribas”, São Paulo, Brazil). The molecular-level-sensitive, multiplexing quantitative and qualitative FTIR data probed on 1 µL of dried biofluid was compared to signal-to-cutoff index of chemiluminescent immunoassays CLIA and ELISA (IgG antibodies against SARS-CoV-2). Our main result indicated that 1702–1785 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {cm}^{-1}$$\end{document}cm-1 spectral window (carbonyl C=O vibration) is a spectral marker of the degree of IgG glycosylation, allowing to probe distinctive sub-populations of COVID-19 patients, depending on their degree of severity. The specificity was 87.5 % while the detection rate of true positive was 100%. The computed area under the receiver operating curve was equivalent to CLIA, ELISA and other ATR-FTIR methods (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>0.85$$\end{document}>0.85). In summary, overall discrimination of healthy and COVID-19 individuals and severity prediction as well could be potentially implemented using micro-FTIR reflectance spectroscopy on blood serum samples. Considering the minimal and reagent-free sample preparation procedures combined to fast (few minutes) outcome of FTIR we can state that this technology is suitable for fast screening of immune response of individuals with COVID-19. It would be an important tool in prospective studies, helping investigate the physiology of the asymptomatic, oligosymptomatic, or severe individuals and measure the extension of infection dissemination in patients.

Towards the direct detection of viral materials at the surface of protective face masks via infrared spectroscopy

The ongoing COVID-19 pandemic represents a considerable risk for the general public and especially for health care workers. To avoid an overloading of the health care system and to control transmission chains, the development of rapid and cost-effective techniques allowing for the reliable diagnosis of individuals with acute respiratory infections are crucial. Uniquely, the present study focuses on the development of a direct face mask sampling approach, as worn (i.e., used) disposable face masks contain exogenous environmental constituents, as well as endogenously exhaled breath aerosols. Optical techniques—and specifically infrared (IR) molecular spectroscopic techniques—are promising tools for direct virus detection at the surface of such masks. In the present study, a rapid and non-destructive approach for monitoring exposure scenarios via medical face masks using attenuated total reflection infrared spectroscopy is presented. Complementarily, IR external reflection spectroscopy was evaluated in comparison for rapid mask analysis. The utility of a face mask-based sampling approach was demonstrated by differentiating water, proteins, and virus-like particles sampled onto the mask. Data analysis using multivariate statistical algorithms enabled unambiguously classifying spectral signatures of individual components and biospecies. This approach has the potential to be extended towards the rapid detection of SARS-CoV-2—as shown herein for the example of virus-like particles which are morphologically equivalent to authentic virus—without any additional sample preparation or elaborate testing equipment at laboratory facilities. Therefore, this strategy may be implemented as a routine large-scale monitoring routine, e.g., at health care institutions, nursing homes, etc. ensuring the health and safety of medical personnel.