Fast Screening of Tuberculosis Patients Based on Analysis of Plasma by Infrared Spectroscopy Coupled with Machine Learning Approaches

Prompt diagnosis of tuberculosis (TB) enables timely treatment, limiting spread and improving public health for this disease. Currently, a rapid, sensitive, accurate, and cost-effective detection of TB still remains a challenge. For this purpose, we engaged a transmission skill and an attenuated total reflectance (ATR) technique coupled with Fourier-transform infrared spectrometry (FTIR) to study the IR spectra of the plasma samples from TB patients (<i>n</i> = 10) and healthy individuals (<i>n</i> = 10). To ensure high-quality spectral data, spectra were collected in both transmission and ATR modes, with each measurement consisting of 256 scans at a resolution of 8 cm^-1. For the transmission mode, measurements were repeated five times per sample, while ATR-FTIR measurements were repeated three times per sample. These parameters were carefully optimized through rigorous testing to achieve the highest possible signal-to-noise ratio for patient sample analysis. Using this method, we obtained a total of 100 spectra from 20 samples in the transmission mode and 60 spectra in the ATR-FTIR mode, ensuring sufficient data for robust spectral analysis. Further, we applied machine learning techniques to analyze and classify the IR spectra; by this means, we differentiated those spectra between TB patients and healthy ones. In this work, we modified the transmission-FTIR setup to improve the absorption sensitivity by focusing the IR light on the interface of the sample; while, we used a high-refractive-index crystal ZnSe as a medium to reflect the signals in ATR scheme. Routinely, we compared the spectra obtained from both methods; in their second derivative curves, we notified that there had distinct spectral differences in protein and lipid regions (3500-3000, 2900-2800, and 1700-1500 cm^-1) between TB and healthy groups. Using three machine learning classifiers-Logistic Regression (LR), Random Forest (RF), and XGBoost (Xg)-we found that the Xg achieved an accuracy of 0.749, precision of 0.703, recall of 0.901, F1 score of 0.790, and an AUC of the ROC curve of 0.82 for absorption spectra in the 3500-2700 cm^-1 region; additionally, the machine learning practice showed that ATR data possessed performance parameters of ∼ 80% in accuracy. We randomly assigned participants (rather than individual scans) to 80% training and 20% test sets to train and validate three machine learning models (LR, RF, and Xg). Based on the results, we concluded that the absorption spectroscopic method demonstrated its superior performance in TB diagnosis. Thus, we have showed that absorption-FTIR spectroscopy is a valuable tool for sorting the TB disease from patients. The spectral IR analysis of plasmas can complement clinical evidence and provides a rapid and accurate diagnosis of TB in clinic.