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

The paper presents a simulation study of breath analysis based on theoretical models of MEMS cantilever sensor array. The purpose of this study is to suggest a methodology for development of MEMS electronic nose for monitoring disease specific volatiles in the exhaled breath. The oxidative stress and diabetes have been taken as prototype case studies for 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 have been taken as interferents. The MEMS cantilevers are coated with volatile selective polymers and are analyzed in both static and dynamic modes. The sensor array is defined by polymer selection based on three data mining methods: principal component analysis, fuzzy c-means clustering and fuzzy subtractive clustering. This utilizes vapor/polymer partition coefficients as data base. The analyses are carried out to find optimal combinations of polymer selection method and cantilever sensing mode. The virtual breath analysis experiments are analyzed by principal component analysis 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 fuzzy subtractive clustering; the isoprene (hypoxia) is detectable only in static sensing mode with polymers selected by fuzzy c-means clustering; and, the ethane (oxidative stress) detection becomes possible by all sensing modes and polymer selections provided the breath samples are preconcentrated. The 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 optimal combination of sensing mode and polymer coatings.