As a baseline, we calculated the MAE of predictions obtained from just the mean product values of the training runs. This gave an MAE of 7.0%. The four datasets were combined with the four models to produce all possible combinations. The results of these are shown in Figure . The Yeo-Johnson transformation improved results when using a linear regression model (Untransformed: 4.2% and transformed 3.8% MAE). The results for each algorithm with the no colour dataset (MAE 7.0, 7.1, and 8.2%) were all comparable to or worse than the baseline results (7.0%). This suggests the Pearson correlation coefficients identified the useful features in this context, and there is little value in the non-colour data in isolation. The best predictions were obtained using polynomial regression with cumulative green (MAE 3.6%). However, this required the prior step of feature identification. A gradient boosting regression model made predictions which were nearly as accurate (MAE 3.9%) but with all the features. These predictions were more accurate than those obtained using a gradient boosting regression model with cumulative green as the sole feature in contrast to all other models, suggesting there is predictive value in the remaining data when using a suitable model. Using all features has the advantage of obviating the need for prior feature selection. This does not interfere with the interpretability of the model either, as feature importance values can be extracted from the decision tree models. Feature importance using all features in a gradient boosting regression model (Figure in the Supplementary Information) further confirms colour to be the most informative feature and suggests that it is likely all four models identify similar patterns in the relationship between cumulative green and product formation. in the associated datasets. Given that the input data is a time-series signal, this raises the question of whether a time-series model would be more accurate.