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In Webster's Seventh New Collegiate Dictionary, drift is defined as "a gradual change in any quantitative characteristic that is supposed to remain constant". Thus, a drifting chemical sensor does not give exactly the same response even if it is exposed to exactly the same environment for a long time. Drift is a common problem for all chemical sensors, and thus needs to be considered as soon as measurements are made for a long period of time.First in this chapter, possible reasons for drift will be discussed. A distinction is made between drift in the sensors, and drift in the measurement system. After this, typical features of drift as seen in the measurements will be shown. These features include gradual increase or decrease, and jumps in the responses. At the end, many different methods for reducing the effects of drift will be described. These drift reduction methods try to compensate for the changes in sensor performance using mathematical models and thus maintaining the gas identification capability of the electronic nose. Many different methods have been applied for different situations. It is impossible to compare all the methods since each one of them makes some assumptions of how the measurements are made and/or how the drift is manifested. Not all examples discussed are for measurements with electronic noses, but the concepts may easily be transferred also to such applications. The purpose of describing all the methods is to show some possible ways of reasoning when dealing with a data-set from drifting sensors.13.1

In this paper, various preprocessing methods were tested on data generated by X-ray powder diffraction (XRPD) in order to enhance the partial least-squares (PLS) regression modeling performance. The preprocessing methods examined were 22 different discrete wavelet transforms, Fourier transform, Savitzky–Golay, orthogonal signal correction (OSC), and combinations of wavelet transform and OSC, and Fourier transform and OSC. Root mean square error of prediction (RMSEP) of an independent test set was used to measure the performance of the various preprocessing methods. The best PLS model was obtained with a wavelet transform (Symmlet 8), which at the same time compressed the data set by a factor of 9.5. With the use of wavelet and X-ray powder diffraction, concentrations of less than 10% of one crystal from could be detected in a binary mixture. The linear range was found to be in the range 10–70% of the crystalline form of phenacetin, although semiquantitative work could be carried out down to a level of approximately 2%. Furthermore, the wavelet-pretreated models were able to handle admixtures and deliberately added noise.

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