In catalyst development, tests to measure performances can be extremely time-consuming and expensive. In this study, we have explored the possibility of using faster and less expensive characterization data to validate mathematical models linking production parameters and performances. Specifically, we first modeled the production parameters to the performance data available in our dataset, using generalized regression, and from this model, we predicted a set of optimal production parameters. We then analyzed the Infrared Spectroscopy (IR) data using the Wavelet model in Functional Data Explorer plugging the production parameters as additional parameters. Thanks to this addition we were finally able to generate a synthetic spectrum for an optimal catalyst. The generated spectra combined with the predicted production parameters can be used by the scientist to more quickly understand the underlying mechanisms driving performances. Finally, a new catalyst material developed using the predicted parameters can be analyzed using IR and the synthetic spectra can be used to validate the model.