This article presents a signal-processing technique capable of canceling the effect of background chemicals from the multivariate response of a sensor array. We propose a generalization of the Fisher's eigenvalue solution that minimizes the discrimination between undesirable chemicals and a neutral reference. The proposed technique is a generalization of an earlier model that was limited to the removal of single volatiles. A reformulation of class memberships allows the new model to cancel the effect of both single and mixture backgrounds. The model is validated on experimental data from an array of temperature-modulated metal-oxide sensors exposed to binary and ternary mixtures.
|Number of pages||4|
|State||Published - 2004|
|Event||IEEE Sensors 2004 - Vienna, Austria|
Duration: Oct 24 2004 → Oct 27 2004
|Conference||IEEE Sensors 2004|
|Period||10/24/04 → 10/27/04|