Laser-Based Infrared Spectroscopy of Human Breath for Non-Invasive Detection of Metabolic Disorders

Abstract

Exhaled breath contains hundreds of volatile organic compounds (VOCs) that reflect the metabolic state of the human body. Non-invasive analysis of these molecules presents a promising path for early disease diagnosis, particularly for metabolic disorders such as type 1 diabetes mellitus (T1DM). In this study, we present a novel laser-based platform for the detection of key breath biomarkers using mid-infrared absorption spectroscopy with a quantum cascade laser (QCL).

The experimental setup includes a tunable QCL operating from 5.3 to 12.8 μm, a multipass Herriott cell with an optical path length of 76 meters, and a precision gas sampling module. We validated the system using certified gas mixtures and subsequently analyzed exhaled breath samples from 120 volunteers, including 60 healthy individuals and 60 pediatric patients with confirmed T1DM.

Our results demonstrate that breath concentrations of acetone, ethanol, and isopropanol differ significantly between healthy and diabetic subjects. Detection limits below 100 ppb were achieved for each target compound. The developed methodology provides a robust, real-time, and non-invasive means of assessing metabolic health through breath analysis.

This work lays the foundation for portable breath diagnostic devices and highlights the potential of integrating laser photonics with clinical diagnostics. Further research will focus on expanding the biomarker panel and incorporating machine learning for advanced pattern recognition in spectral data.

Speaker

Olga A. Nebritova
Bauman Moscow State Technical University, Moscow, Russia
Russia

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