Investigation of the multivariate calibration methods for gas sensors

In many electronic nose applications where gas sensors utilizing for a long time, there is an undesirable drift effect on the sensors, which affects the classification quality negatively. Although the sensor drift is inevitable, it is possible to reduce this effect with the calibration transfer methods. This paper presents a comparison study of various multivariate standardization methods to facilitate an effective calibration way on a comprehensive dataset, which is reachable on-line. In this study, three methods applied: direct standardization (DS), orthogonal signal correction (OSC) and piecewise direct standardization (PDS). In addition, these three methods are applied the data, which consisted of selected features. The results have shown that the classification success has increased with multivariate calibration technique applied to the selected features. The results also demonstrate that using the best features in the signal processing part can play an important role for the calibration success. This outcome may lead to a new perspective for the future works.