Spectroscopic methods are gaining in popularity in biotechnology because of their ability to deliver rapid, noninvasive measurements of the concentrations of multiple chemical species. Such measurements are particularly necessary for the implementation of control schemes for cell culture bioreactors. One of the major challenges to the development of spectroscopic methods for bioreactor monitoring is the generation of accurate and robust calibration models, particularly because of the inherent variability of biological processes. We have evaluated several methods of building calibration models, including synthetic calibrations and medium spiking methods. The approach that consistently produced reliable models incorporated samples removed from a bioreactor that were subsequently altered so as to increase the sample variation. Several large volume samples were removed from a bioreactor at varying time points and divided into multiple aliquots to which were added random, known amounts of the analytes of interest. Near-infrared spectra of these samples were collected and used to build calibration models. Such models were used to quantify analyte concentrations from independent samples removed from a second bioreactor. Prediction errors for alanine, glucose, glutamine, and leucine were 1.4, 1.0, 1.1, and 0.31 mM, respectively. This adaptive calibration method produces models with less error and less bias than observed with other calibration methods. Somewhat more accurate measurements could be attained with calibrations consisting of a combination of synthetic samples and spiked medium samples, but with an increase in calibration development time.