Laboratory evaluations demonstrate improved breath analysis accuracy in distinguishing CO levels in smokers and nonsmokers, indicating clinical potential for diagnostics.
TDLAS-based breath sensors hold significant promise for clinical diagnostics because of their high selectivity, fast response, and operational simplicity. However, their application in high-precision, multicomponent breath analysis is often hindered by system complexity, high cost, and limited detection accuracy. To overcome these challenges, this study presents a near-infrared TDLAS system for the simultaneous monitoring of carbon monoxide (CO), carbon dioxide (CO₂), and methane (CH₄) in breath. The system integrates a spectral line optimization and selection strategy with a novel, miniaturized dual-path multipass cell and time-division multiplexing (TDM). To reduce the complexity of the system and improve the measurement accuracy, a closely spaced spectral line pair (CO: 4285.0 cm⁻¹; CH₄: 4284.5 cm⁻¹) within the 2.3 μm band was selected for simultaneous detection using a single laser. The custom-designed multipass cell achieves an 88.52 m optical path within a 300 mL volume, ensuring both high sensitivity and compatibility with small breath samples. Furthermore, by leveraging the strong absorption lines of carbon dioxide within the 2 μm band, a dedicated 15 cm optical path was integrated into the multipass cell. Combined with the TDM technique, this approach enabled a compact dual-path, single-detector architecture, which significantly improved the integration level of the system. Laboratory evaluations demonstrated excellent linearity, with detection limits of 1.49 ppb for CO, 1.86 ppb for CH₄, and 720.2 ppb for CO₂, as determined by Allan deviation analysis. The system was successfully deployed in breath testing, effectively distinguishing end-tidal CO levels between smokers and nonsmokers, with concentration trends consistent with those in the established literature. This work validates a reliable and practical TDLAS sensor for real-time, multicomponent breath analysis, offering a viable pathway toward noninvasive, high-sensitivity diagnostic devices.
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Ou et al. (2026) studied this question.
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