Predictive models of ethanol concentrations in simulated exhaled breath and exhaled breath condensate under varied sampling conditions

Abstract

Breath monitoring is a non-invasive, safe, and repeatable approach to determining the respiratory, gastrointestinal, and general health status of humans and other mammals. Breath samples could be detected in two ways—directly sensing exhaled breath (EB) or chilling the EB to obtaining the exhaled breath condensate (EBC). Each has its advantages and disadvantages but they are both affected by different sampling conditions. The dearth of information on how sampling conditions affect the intrinsic properties of biomarkers in breath hinders the use of breath monitoring in clinical use. In this study, ethanol, a potential biomarker of liver function, was chosen as a model biomarker to demonstrate the effect of sampling conditions on different phases and how breath sampling could be standardized by developing predictive models. EB and EBC samples were determined under three simulated breath temperatures, two breath rates, and two condensing temperatures for developing predictive models. Results showed EB samples were affected by breath temperatures and EBC samples were affected by condensing temperatures. Flow rate changes did not have a significant influence on both EB and EBC samples. Final predictive models based on 5 minute sensing time were developed for EB ( R 2 = 0.8261) and EBC ( R 2 = 0.9471).