The first machine learning models we investigate are interpretive models known to perform well in most general cases. Here we use Decision Trees (DT), Generalized Linear Models (GLM), k-Nearest Neighbor regression (KNN), Partial Least Squares (PLS), linear Support Vector Machine (SVM L ), radial Support Vector Machine (SVM R ), Random Forest (RF), and a Multi-Layer Perceptron (MLP) to predict TD and %LC from the storage conditions (pH, buffer type, %EB, histamine concentration) and the time-point. We evaluated each machine learning model using both bootstrapping cross-validation and a k-fold cross-validation using k=2, 5, 10, and 20. To compare the models to each other and across validation techniques, we calculated the R 2 of the validation set. These results are summarized in Figure and Table with additional metrics shown in Figures S1. It can be seen that SVM R , RF, and MLP models out-perform the other models and within error of each other. For these models, the performance of models trained on 50% of the available data (k=2) is similar to those trained on 95% of the available data (k=20). This indicates that these models can be trained with a small amount of data and still be predictive. Since these models all perform within error of each other, we compared the true TD to the predicted TD for each fold for the three models. The results for k=2 cross-validation is shown in Figures negative transformations into account. The weights for the "Histidine" input variable are all negative. Additionally, we calculated the importance of all the importance variables using Olden's method (bottom of Figure ). It is not surprising that the most important variable is the Time-point, followed by the pH (see the above discussion of Figure ). While these results do not yield any insights into how these variables affect chemical degradation, they do show that this type of analysis can be used to reveal known relationships between the input variables and the output variables.