Active classification with arrays of tunable chemical sensors

• We present active-sensing method for classification with tunable chemo-sensor arrays.
• Objective functions to quantify discriminatory information of array configurations.
• The method was tested on a database of IR absorption spectra with 250 analytes.
• The method was experimentally validated on an array of metal-oxide chemical sensors.
• Results show that the active method is more accurate and noise-robust than passive.

This paper presents Posterior-Weighted Active Search (PWAS), an active-sensing algorithm for classification of volatile compounds with arrays of tunable chemical sensors. The algorithm combines concepts from feature subset selection and sequential Bayesian filtering to optimize the sensor array tunings on-the-fly based on information from previous measurements. Namely, the algorithm maintains an estimate of the posterior probability associated with each chemical class, and updates it sequentially upon arrival of each new sensor observations. The updated posteriors are then used to bias the selection of the next sensor tunings towards the most likely classes, in this way reducing the number of measurements required for discrimination. We characterized PWAS on a database of infrared absorption spectra with 250 analytes, and then validated it experimentally on an array of metal-oxide sensors. Our results show that PWAS outperforms passive-sensing approaches based on sequential forward selection, both in terms of classification performance and robustness to noise in sensor measurements.