Use of NIR in COVID-19 Screening: Proof of Principles for Future Application

Abstract

The COVID-19 pandemic that affected the world between 2019 and 2022 showed the need for new tools to be tested and developed to be applied in global emergencies. Although standard diagnostic tools exist, such as the reverse-transcription polymerase chain reaction (RT-PCR), these tools have shown severe limitations when mass application is required. Consequently, a pressing need remains to develop a rapid and efficient screening test to deliver reliable results. In this context, near-infrared spectroscopy (NIRS) is a fast and noninvasive vibrational technique capable of identifying the chemical composition of biofluids. This study aimed to develop a rapid NIRS testing methodology to identify individuals with COVID-19 through the spectral analysis of swabs collected from the oral cavity. Swab samples from 67 hospitalized individuals were analyzed using NIR equipment. The spectra were preprocessed, outliers were removed, and classification models were constructed using partial least-squares for discriminant analysis (PLS-DA). Two models were developed: one with all the original variables and another with a limited number of variables selected using ordered predictors selection (OPS-DA). The OPS-DA model effectively reduced the number of redundant variables, thereby improving the diagnostic metrics. The model achieved a sensitivity of 92%, a specificity of 100%, an accuracy of 95%, and an AUROC of 94% for positive samples. These preliminary results suggest that NIRS could be a potential tool for future clinical application. A fast methodology for COVID-19 detection would facilitate medical diagnoses and laboratory routines, helping to ensure appropriate treatment.

Figure 1

A) Score plot for Principal Component Analysis for all samples (PC1 X PC2). Red dots represent the positive and blue dots represent the negative samples. The ellipse shows a confidence interval of 95%. B) NIR spectra of the samples. Red lines represent the outliers removed, and black lines are the samples kept for modeling.

Figure 2

A) All raw NIR spectra after outlier removal. Blue lines represent negative samples, and red lines represent positive samples. B) Mean of positive (red) and negative (blue) groups from raw NIR spectra after outlier removal.

Figure 3

A) Receiver Operating Characteristic (ROC) curve with sensitivity and specificity from the calibration set. B) ROC curve with sensitivity and specificity from the prediction set. The blue line represents negative samples, and the orange line represents positive samples.