Model-based process design is increasingly popular when designing pharmaceutical purification processes. The effect of uncertainties in concentration measurements on the estimation of model parameters is analyzed for two cases of non-isocratic adsorption chromatography. A model, calibrated to experiments, is used to generate data by adding a Monte Carlo sampled error in the inlet concentrations. New model parameters are estimated by minimizing the deviation between the synthetic data and the model. The first case is a separation of rare earth elements by ion-exchange chromatography and the second case is a purification of insulin from a product-related impurity by reversed-phase chromatography. It is shown that normally distributed errors in the concentrations result in deviations in the UV-signal that are not normally distributed. With the applied method, known concentration distributions can be translated into probability distributions of the model parameters, which can be taken into account in the model-based process design. (C) 2013 Elsevier Ltd. All rights reserved.