Arora, M.; Zambrzycki, S.C.; Levy, J.M.; Esper, A.; Frediani, J.K.; Quave, C.L.; Fernández, F.M.; Kamaleswaran, R.
Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS. Metabolites 2022, 12, 232.
https://doi.org/10.3390/metabo12030232
AMA Style
Arora M, Zambrzycki SC, Levy JM, Esper A, Frediani JK, Quave CL, Fernández FM, Kamaleswaran R.
Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS. Metabolites. 2022; 12(3):232.
https://doi.org/10.3390/metabo12030232
Chicago/Turabian Style
Arora, Mehak, Stephen C. Zambrzycki, Joshua M. Levy, Annette Esper, Jennifer K. Frediani, Cassandra L. Quave, Facundo M. Fernández, and Rishikesan Kamaleswaran.
2022. "Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS" Metabolites 12, no. 3: 232.
https://doi.org/10.3390/metabo12030232
APA Style
Arora, M., Zambrzycki, S. C., Levy, J. M., Esper, A., Frediani, J. K., Quave, C. L., Fernández, F. M., & 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
