Related Experiment Video For breath sensor system
Updated: Aug 7, 2025

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry
Published on: March 9, 2018
Chemiresistive Sensor Array with Nanostructured Interfaces for Detection of Human Breaths with Simulated Lung Cancer
Guojun Shang1, Dong Dinh2, Tara Mercer1
1Department of Chemistry, State University of New York at Binghamton, Binghamton, New York 13902, United States.
Abstract:
Timely screening of lung cancer represents a challenging task for early diagnosis and treatment, which calls for reliable, low-cost, and noninvasive detection tools. One type of promising tools for early-stage cancer detection is breath analyzers or sensors that detect breath volatile organic compounds (VOCs) as biomarkers in exhaled breaths. However, a major challenge is the lack of effective integration of the different sensor system components toward the desired portability, sensitivity, selectivity, and durability for many of the current breath sensors. In this report, we demonstrate herein a portable and wireless breath sensor testing system integrated with sensor electronics, breath sampling, data processing, and sensor arrays derived from nanoparticle-structured chemiresistive sensing interfaces for detection of VOCs relevant to lung cancer biomarkers in human breaths. In addition to showing the sensor viability for the targeted application by theoretical simulations of chemiresistive sensor array responses to the simulated VOCs in human breaths, the sensor system was tested experimentally with different combinations of VOCs and human breath samples spiked with lung cancer-specific VOCs. The sensor array exhibits high sensitivity to lung cancer VOC biomarkers and mixtures, with a limit of detection as low as 6 ppb. The results from testing the sensor array system in detecting breath samples with simulated lung cancer VOC constituents have demonstrated an excellent recognition rate in discriminating healthy human breath samples and those with lung cancer VOCs. The recognition statistics were analyzed, showing the potential viability and optimization toward achieving the desired sensitivity, selectivity, and accuracy in the breath screening of lung cancer.
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