We selected Random Forest (RF) and Multi-layered Perceptron (MLP) to test on IR spectra to determine if there was a need for using neural networks (MLP) as compared to ensemble methods (RF). An unoptimized MLP consistently outperformed RF models (Figure ) with an average functional group F1-score of 0.771 for the MLP model compared to 0.650 for RF (see Table , S2 for F1-score of each model and Figure and the experimental section for definitions of F1 score). We trained the MLP to predict all functional groups simultaneously as one multilabel classifier. In order to evaluate the effect of transfer learning that has been previously done for MLP , we also evaluated 13 binary classifiers in addition to the 1 multi-label network. The binary classifier approach did not improve the performance of the MLP model significantly as these models only produced an improvement in functional group F1 score of 0.006 over the multi-label model, suggesting that transfer learning is not a significant factor in the multi-label network.