Inclusion of storage conditions improves the predictions significantly, decreasing the MAE of the models to 0.121, 0.132, and 0.148, respectively and increases all the R 2 values to be greater than 0.9. These results show that the inclusion of ThioT data into the model is beneficial. Furthermore, the MAE values for the TD prediction are approximately half that for the model trained with ThioT versus the model trained on only storage conditions, indicating that that there is value in adding physical degradation measures to the model.