The successful modeling of TD using storage conditions was repeated for the prediction of potency (quantitated as %LC). In Figure it can be seen that the RF model achieved an R 2 greater than 0.90 and Figure indicates that the RF model has the lowest RMSE and MAE. Additionally, the relationship between the true observations and predictions does not show any significant patterns (Figure , Figure ). For other interpretable machine learning models, points with true low %LC values are predicted to be too high (Figures ). These results indicate that the RF model is able to capture the relationship between the input variables and %LC. An analysis of the variable importance is given in Figure . As with the prediction of TD, the most important variable for the prediction of %LC is the Timepoint of the data. The second most important variable is the type of buffer used to store the peptide. This result is interesting as the only pH condition associated with a change in buffer type is the pH 8.7 condition, indicating that the RF model is using this variable as a surrogate for the highest pH condition. Since the highest pH condition shows the greatest decrease in %LC, this selection is justified. The third most important variable is the pH, which likely explains the remaining variance in the %LC curve not already covered by the selection of buffer type. Finally, the EB is least important variable that is used by the RF model and likely adjusts for small changes in %LC. It is interesting to note that the amount of histidine is not used in the model to predict %LC and is assigned an importance of zero, similar to the result from the TD model.