Different spectroscopic approaches have proved to be excellent analytical tools for monitoring process-induced transformations of active pharmaceutical ingredients during pharmaceutical unit operations. In order to use these tools effectively, it is necessary to build calibration models that describe the relationship between the amount of each solid-state form of interest and the spectroscopic signal. In this study, near-infrared (NIR) and Raman spectroscopic methods have been evaluated for the quantification of hydrate and anhydrate forms in pharmaceutical powders. Process type spectrometers were used to collect the data and the role of the sampling procedure was examined. Multivariate regression models were compared with traditional univariate calibrations and special emphasis was placed on data treatment prior to multivariate modeling by partial least squares (PLS). It was found that the measured sample volume greatly affected the performance of the model whereby the calibrations were significantly improved by utilizing a larger sampling area. In addition, multivariate regression did not always improve the predictability of the data compared to univariate analysis. The data treatment prior to multivariate modeling had a significant influence on the quality of predictions with standard normal variate transformation generally proving to be the best preprocessing method. When the appropriate sampling techniques and data analysis methods were utilized, both NIR and Raman spectroscopy were found to be suitable methods for the quantification of anhydrate/hydrate in powder systems, and thus the method of choice will depend on the conditions in the process under investigation.