MEMS sensor array-based electronic nose for breath analysis—a simulation study

Abstract

The paper presents a simulation study of breath analysis based on theoretical models of microelectromechanical structure (MEMS) cantilever sensor array. The purpose of this study is to suggest a methodology for the development of MEMS electronic nose (e-nose) for monitoring disease-specific volatiles in exhaled breath. Oxidative stress and diabetes are taken as case studies for the assessment of e-nose designs. The detection of ethane for general oxidative stress, isoprene for hypoxia, and acetone for diabetes are considered for targeted detection. A number of volatiles concurrently present in the exhaled breath are taken as interferents. The MEMS cantilevers are coated with volatile-selective polymers and are analyzed in both the static and dynamic modes. The sensor array is defined by polymer selections based on three data mining methods: principal component analysis (PCA), fuzzy c-means clustering (FCM), and fuzzy subtractive clustering (FSC). This utilizes vapor/polymer partition coefficients as a database. Analyses are carried out to find optimal combinations of the polymer selection method and cantilever sensing mode. Virtual breath analysis experiments are analyzed by PCA for target discrimination. It is found that no single combination works best in all conditions. The acetone (diabetes) detection is best in both sensing modes with the polymers selected by FSC; the isoprene (hypoxia) is detectable only in static sensing mode with polymers selected by FCM clustering; and the ethane (oxidative stress) detection is possible by all sensing modes and polymer selections, provided the breath samples are preconcentrated. This study suggests that it is difficult to realize a single general-purpose MEMS breath analyzer. The dedicated analyzers for specific disease indications can however be made with an optimal combination of sensing mode and polymer coatings.