A Mobile E-Nose Prototype for Online Breath Analysis

Abstract

Exhaled breath provides information of individual's holistic physiological condition, making non-invasive exhaled breath testing a convenient and effective choice for personalized health management. This work proposes a mobile e-nose prototype equipped with a miniaturized gas delivering unit, an advanced sensor array, a customized analog to digital conversion circuit board, an online data transmission, and machine learning algorithms that can effectively detect and analyze volatile compounds in exhaled breath, providing a systematic design strategy for non-invasive disease diagnosis. The gas sensor array, as a core element, is composed of eight graphene-based chemiresistive materials which are sensitive to trace gas analytes. In an online breath test study with 401 cases, patients with respiratory diseases can be distinguished from the healthy with an overall accuracy higher than 80%. In addition, classification of breath samples between patients with chronic obstructive pulmonary disease and the healthy higher than 85% is achieved. These results indicate the proposed portable e-nose prototype holds great promise in personalized health state monitoring.