Electronic nose signal restoration—beyond the dynamic range limit

When measuring over-concentrated stimuli, chemical sensors tend to exhibit corrupted time signals, which are normally categorized as missing data. Such a failure of one or more sensors occurs frequently in applications where an eNose is exposed to a diverse repertoire of chemicals. As a rule, missing data are removed from the dataset by leaving a potentially large portion of the original dataset unutilized. Here we propose an algorithm to handle such missing data by utilizing intact regions of corrupted signals to restore the damaged regions. We do so by fitting a parametric model of the sensor response over time to the intact regions, and using the resulting model for the restoration. We show that the restoration is both accurate and consistent, thus allowing for the restored signals to take part in any subsequent data analysis process.