Before breath-based diagnostics for lung infections can be implemented in the clinic, it is necessary to understand how the breath volatiles change during the course of infection, and ideally, to identify a core set of breath markers that can be used to diagnose the pathogen at any point during the infection. In the study presented here, we use secondary electrospray ionization-mass spectrometry (SESI-MS) to characterize the breathprint of Pseudomonas aeruginosa and Staphylococcus aureus lung infections in a murine model over a period of 120 h, with a total of 86 mice in the study. Using partial least squares-discriminant analysis (PLS-DA) to evaluate the time-course data, we were able to show that SESI-MS breathprinting can be used to robustly classify acute P. aeruginosa and S. aureus mouse lung infections at any time during the 120 h infection/clearance process. The variable importance plot from PLS indicates that multiple peaks from the SESI-MS breathprints are required for discriminating the bacterial infections. Therefore, by utilizing the entire breathprint rather than single biomarkers, infectious agents can be diagnosed by SESI-MS independent of when during the infection breath is tested.