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Background/Aim: Untargeted metabolomics enable the exploration of pathways that are affected by environmental respiratory exposures. However, traditional analyses of such data analyze each feature separately, ignoring interactions between features and reducing power. Independent component analysis (ICA) is a machine learning method that uncovers hidden patterns in data and has found widespread application in the analysis of medical imaging, video surveillance, and financial data. We propose that applying ICA to untargeted metabolomics data can alleviate the limitations of traditional feature-by-feature analyses. Methods: We applied ICA to approximately 25,000 untargeted metabolomic features measured in brachial lavage fluid (BALF) collected from 15 healthy participants who experienced separate exposures to Cl₂ gas and clean air. We estimated a total of 7 components using ICA. We compared our results to traditional feature-by-feature analyses. Results: In the traditional analyses, no features remained significant after a false discovery rate (FDR) correction. Of the 7 ICA components, 3 showed statistically significant differences, assessed using paired t-tests, in expression between the two conditions (Cl vs. clean air). These three components (t-statistics: 2.4, 7.2, and 12.2) remain significant after FDR correction. Annotation of detected metabolites in these components suggests variations in oxidative stress-related metabolites, including methionine (an important anti-oxidant) and oxidized fatty acids in the Cl­₂ exposure compared with the clean air exposure. Other features in these components showed a negative mass defect, which is consistent with the presence of halogenated compounds that may form through chlorine nucleophilic substitution. These ICA components were not associated with the age or sex of the participants. Conclusions: Our results suggest Cl₂ exposure influences the lung metabolome and demonstrate the value of using machine learning methods to uncover biological response to respiratory exposures. Disclaimer: The views in this abstract belong to the authors and are not necessarily those of the U.S. Environmental Protection Agency.