To validate our method on compound mixtures, we obtained the IR spectra of three different mixtures of molecules (raw spectra given in Figures ) and predicted all the functional groups of the compounds in the mixture using our MLP-IR machine learning model (see Table ). We stress the point that these spectra are obtained in our lab, are not part of the NIST dataset, and are obtained from instruments different than those used by the NIST as it is essential to validate a machine learning method for practical use in different laboratories. Since these spectra are external to the NIST webbook data, they constitute a 'test set' for our model. The compound mixtures were prepared by mixing two solid compounds and each mixture contained different set of functional groups. Performance metrics, such as molecular F1 score etc., described previously for single molecules are applied to a mixture of molecules by considering the set of all functional groups (a union of all functional groups present in the mixture). For mixture 1, our IRonly method correctly predicted 2 out of the 4 functional groups present in the mixture, and predicted an additional functional group not present in the mixture, yielding an MF1 score of 0.65 (Table ). Given the resolution of spectral data, the lack of an O-H peak above 3500 cm -1 could also lead a human chemist to conclude that no carboxylic acid is present in the mixture (Figure ). Additionally, the presence of a peak near 2940 cm -1 may lead a human to conclude that a methyl group is present in the mixture (Figure ). For mixture 2, we obtain an MF1 score for the mixture of 0.80 as we correctly predict 2 out of the three functional groups present in the mixture and do not predict any additional functional groups. The only missed functional group is the amide group, which is known to be problematic in our model (functional group F1 score < 0.60) and the lack of a strong peak near 1650 cm -1 may contribute to a human's inability to identify this functional group (Figure ). For mixture 3, our method correctly predicts 3 out of the 6 functional groups in the mixture and does not predict any additional groups in the mixture, yielding a molecular F1 score of 0.67. The model was not able to identify a methyl group and a human may make the same mistake given the lack a peak near 2940cm -1 (Figure ). The model also failed to predict the presence of a nitro group and the presence of an ether, potentially due to the peaks corresponding to these groups overlapping with other peaks in the aromatic region of the spectra. Our results show that the deep learning model trained on single compound spectra can give reasonable performance to predict functional groups for mixtures of compounds. Future work entails training on compound mixture spectral data along with using other deep learning architectures, such as Generative Adversarial Networks. This is essential for correctly estimating the limitations of machine learning models for adoption in industry for autonomous instrumentation.