Machine Learning Applied to Chemical Analysis: Sensing Multiple Biomarkers in Simulated Breath Using a Temperature-Pulsed Electronic-Nose

Abstract

Monitoring of chemical species in breath offers an approach for the detection of disease and other conditions that cause homeostatic imbalance. Here, we demonstrate the use of microsensor-based devices for detecting select biomarkers in simulated exhaled breath as a step toward enabling fast and inexpensive breath-screening technology. Microhotplate elements functionalized with three chemiresistive metal-oxide films (SnO(2), In(2)O(3), and CuO) were used to acquire data in simulated breath containing single targets [(5 to 20) μmol/mol ammonia, methanol, and acetone], as well as mixtures of those species. All devices were operated with programmed thermal cycles featuring rapid temperature excursions, during which film resistances were measured. Material-specific temperature programs were optimized to achieve temperature-dependent metal-oxide sensing film conductance levels and target selectivity. A supervised hierarchical machine-learning algorithm using linear discriminant analysis for dimensional reduction of sensing data and discrimination was developed. This algorithm was employed in the classification and quantification of biomarkers. This approach to microsensor data collection and processing was successful in classifying and quantifying the model biomarkers in validation-set mixtures.