Model validation refers to the process of assessing how well the model can make predictions for a set of test data that was not included in the model training and is therefore a crucial part of any machine learning application. To this end, we compiled a set of test data from seven experiments that were conducted independently from the CA acquisition strategy. We evaluated the predictive power of the converged surrogate models for lignin yield and β-O-4 content for both the test set as well as the acquisitions used to train the model. From the comparison of the predictions with the experimentally measured values (Fig. ), we can qualitatively observe that both surrogate models perform well on the test set and that the measured values all fall within one standard deviation of the predictions.