Microsensors in Dynamic Backgrounds: Toward Real-Time Breath Monitoring

We evaluated a microelectromechanical systems (MEMS) microsensor array with temperature-controlled chemi-resistive elements for use as a noninvasive clinical diagnostic tool to detect the presence or absence of trace amounts of disease biomarkers in simulated breath samples. The microsensor environment was periodically altered between air (78% N(2), 21% O(2) by volume, 20% relative humidity) and simulated breath (77% N(2), 16% O(2), 4% CO(2) by volume, 80% relative humidity) samples creating a dynamic background. Acetone, a disease marker for diabetes, was spiked into select simulated breath samples at relevant concentrations (0.5 mu mol/ mol to 8 mu mol/mol) to pose a diagnostic problem for the sensor array. Using standard statistical dimensionality reduction and classification algorithms, we compared the ability of a variety of sensing materials to detect and recognize the disease marker. Our analyses indicate that the porous, doped nanoparticle materials (Sb:SnO(2) microshell films and Nb:TiO(2) nanoparticle films) are best for the recognition problem (acetone present versus absent), but that WO(3) and SnO(2) films are better at the quantification task (high versus low concentrations of acetone).