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. 2018 Sep 5;8:13297. doi: 10.1038/s41598-018-31543-x

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Volatile metabolic fingerprints distinguish CPE from non-CPE. (A) ROC curves for the discrimination between CPE and non-CPE generated using class probabilities for validation set samples. Green: RF; Orange: linear SVM; Blue: PLS-DA. Values in parentheses indicate AUROC. (B) Venn diagram depicting overlap of metabolites between RF (green), linear SVM (orange), and PLS-DA (blue) for the top 34 discriminatory volatile metabolites across all 100 discovery-validation splits. (C) Box plots depicting classification accuracies from RF (green), linear SVM (orange), and PLS-DA (blue) for validation set samples using: (1) the top 34 discriminatory metabolites (left), (2) the top 34 discriminatory metabolites with permuted sample class labels (center), and (3) 34 randomly-selected metabolites with correct sample class labels (right).