The experimental data collection was carried out iteratively in five batches of acquisitions, until convergence, to train two surrogate models representing the extracted lignin yield and the β-O-4 content, respectively. For convenience, we label the batches 0, 1, 2, 3, 4 where 0 corresponds to the initial batch of Sobol points. To visualize a surrogate model, we use 2D contour plots of the predicted target in the (P f , T )-space and refer to such plots as landscapes. The evolution of the extracted lignin yield and β-O-4 content landscapes as new batches of data are acquired using the CA strategy is visualized in Fig. . The figure also includes the predicted standard deviation, which quantifies the prediction uncertainty. We note that for the CA strategy, the standard deviations of both targets are identical, up to differences in scale, since the surrogate models are all based on the same acquisition set. Hence, one set of contours is sufficient to represent the standard deviation of both the lignin yield and β-O-4 content (Fig. , bottom).