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

作者：Lin Mei, Hsiao‐Chi Lu, Hui‐Wen Lin, Sheng‐Wei Pan, Bing‐Ming Cheng, Ton-Rong Tseng, Jia-Yih Feng, Mei-Lin Ho · 发表于：ACS Omega · 年份：2025 · DOI：10.1021/acsomega.4c07990 · 被引用次数：10 · 研究领域：Spectroscopy Techniques in Biomedical and Chemical Research、Digital Imaging for Blood Diseases、Spectroscopy and Chemometric Analyses

High Resolution Image Download MS PowerPoint Slide 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 ( n = 10) and healthy individuals ( n = 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 i...