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.