In this work various aspects of data preprocessing are discussed. Preprocessing of data from multivariate sensor data systems is often necessary to extract relevant information or remove disturbances. Depending on the sensor type different preprocessing techniques have to be used. A number of important problems face the user e.g. drifting sensor data which results in very short-lived calibration models, and complex models with a very high number of variables. A systematic approach to handle and analyse data is necessary. In this work different preprocessing techniques are elaborated to reduce these problems.

Drift is 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. Drift reduction methods try to compensate for the changes in sensor performance using mathematical models and thus maintaining the identification capability of the chemical sensor. The problem with drifting sensor data and thereby short-lived calibration models is overcome using reference samples and smart algorithms utilizing the relation between the reference measurements and the measurements from the samples. This has been studied in two of the papers in this thesis. Two new approaches have been developed and tested using data from real measurements from the electronic nose and tongue.

In industry and science more and more variables are used to describe the process under study which lead to complex models and calculations. In order to increase the interpretability of the measurements, decrease the calculation demand on the computer, and/or to reduce noise an alternative, more compact, representation of the measurement can be made which describes the important features of the measurement well but with a much smaller vector. This data reduction is the topic of three of the papers in this thesis. Two different methods have been used: A double exponential model has been developed to approximate electronic tongue data. The parameters from this model describe the signal and are used as inputs to multivariate models. Secondly, the more general approach wavelet compression with different strategies for selection of wavelet coefficients has also been studied for variable reduction of both electronic tongue data and X-ray powder diffraction data.