The critical difference between inadequate and successful chemometric models is often data preprocessing, i.e. what is done to the data before using PCA, PLS etc. The goal of preprocessing is to remove variation not related to the problem of interest so that the variation of interest is more evident and can be more easily modeled. The variables selected, e.g. spectral regions, can also greatly affect the success of the application. This course focuses on advanced preprocessing methods, including Extended Multiplicative Scatter Correction (EMSC) and Generalized Least Squares (GLS), for improving models. Variable selection techniques, such as interval PLS (iPLS) are also considered. The effect of preprocessing and variable selection on robustness of the final models is also considered.