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Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression) and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing. Figure 3. Evolution of the pattern of response of a SB31 sensor from FIS Japan when the heated voltage is scanned due to changes in the gas composition (mixture of CO and CH4).One of the problems that affect sensor array instruments is the lack of reproducibility in the sensor signals when technologically identical devices are exposed to a chemical under the exact same conditions. Figure 4 shows the voltage waveforms of 11 temperature modulated Metal Oxide Sensors. It is obvious that the pattern of response is slightly different for both analytes, but the waveform scatter is also remarkable, which poses problems during sensor replacement and calibration transfer scenarios (see section 3).

No abstract

Drift is an important issue that impairs the reliability of gas sensing systems. Sensor aging, memory effects and environmental disturbances produce shifts in sensor responses that make initial statistical models for gas or odor recognition useless after a relatively short period (typically few weeks). Frequent recalibrations are needed to preserve system accuracy. However, when recalibrations involve numerous samples they become expensive and laborious. An interesting and lower cost alternative is drift counteraction by signal processing techniques. Orthogonal Signal Correction (OSC) is proposed for drift compensation in chemical sensor arrays. The performance of OSC is also compared with Component Correction (CC). A simple classification algorithm has been employed for assessing the performance of the algorithms on a dataset composed by measurements of three analytes using an array of seventeen conductive polymer gas sensors over a ten month period.