Using Machine Learning to Infer Composition of Complex Chemical Mixtures

Predicting the concentration of each constituent in a complex gas or liquid mixture is an important challenge in many fields of science and technology, ranging from real-time monitoring of automotive exhaust to detecting potentially toxic substances in the air. We employ an array of solid-state sensors to test gas mixtures in a controlled laboratory environment, recording voltage responses from the sensor array. The sensors in the array typically react to more than one gas in the mixture and their voltage responses are non-linear, making the task of decoding compositions of gas mixtures highly non-trivial. We have developed a Bayesian algorithm which, given a set of readings from the array, identifies and quantifies all gases present in the system. The Bayesian nature of our approach allows us to estimate the uncertainty of the predictions in a rigorous manner and to carry out model selection. Our machine learning framework can be used to model any non-linear system with correlations between inputs and has applications in a wide variety of settings.