APA (7th ed.) Citation

Arora, M., Zambrzycki, S. C., Levy, J. M., Esper, A., Frediani, J. K., Quave, C. L., . . . Kamaleswaran, R. (2022). Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS. Metabolites, 12(3), 232. https://doi.org/10.3390/metabo12030232

Chicago Style (17th ed.) Citation

Arora, Mehak, Stephen C. Zambrzycki, Joshua M. Levy, Annette Esper, Jennifer K. Frediani, Cassandra L. Quave, Facundo M. Fernández, and Rishikesan Kamaleswaran. "Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS." Metabolites 12, no. 3 (2022): 232. https://doi.org/10.3390/metabo12030232.

MLA (9th ed.) Citation

Arora, Mehak, et al. "Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS." Metabolites, vol. 12, no. 3, 2022, p. 232, https://doi.org/10.3390/metabo12030232.