An array of supervised ML models was tuned with 10-fold cross-validation on training data and then were evaluated against the test dataset to provide R 2 and RMSE (root mean square error) values. As shown in Figure , SIMPLS (Statistically Inspired Modification of the Partial Least Squares), and kNN (k-Nearest Neighbors) algorithms showed moderate predictive performance on the test dataset with R 2 values of 0.56 and 0.59, respectively. A random forest (RF) model provided better performance with R 2 = 0.79. A single hidden layer neural network (NNET) delivered improvement over these methods, providing a R 2 value of 0.82, with RMSE of 14.0% and MAE (mean absolute error) of 12.1%.