Comparative analysis of feature annotation methods for SESI-HRMS in exhaled breath analysis.

• Online SESI-HRMS aids real-time EBC but lacks separation for compound differentiation. • EBC analyzed using GC–MS and LC-MS can supplement SESI-HRMS data effectively. • SESI-HRMS shows higher sensitivity for some compounds, needing GC–MS and LC-MS annotation. • Optimizing fragmentation techniques is crucial for comprehensive analysis in this field. Secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS) is a powerful method for the analysis of exhaled breath in real time. However, feature annotation is challenging due to the flow-injection nature of the technique. To evaluate alternative methods for enhancing feature annotation, a study was conducted where the exhaled breath of sixteen subjects was condensed and analyzed using dynamic headspace vacuum in-trap extraction gas chromatography-mass spectrometry (DHS-V-ITEX-GC–MS) and liquid chromatography coupled to mass spectrometry (LC-MS) using polar and reverse-phase conditions along with a data-independent MS2-acquisition method based on multiple injections. The annotation results obtained from these methods were compared to those from SESI-HRMS. The use of these techniques on breath condensate is unprecedented. The GC–MS method primarily detected compounds of exogenous origin, particularly additives in oral hygiene products like menthol. On the other hand, LC-MS detected a vast number of features, especially with the utilized data-independent acquisition method. Chemical classes to these features were assigned in-silico. In positive ion mode, mostly amino acids and amines were detected, while the largest group in negative ion mode consisted of carboxylic acids. Approximately 25% and 5% of SESI features had a corresponding match with LC-MS and GC–MS. While both GC–MS and LC-MS methods partially overlapped with the SESI features, there was limited overlap of both in the mass-to-charge range from 150 to 200. In conclusion, both GC–MS and LC-MS analysis of breath condensate can serve as supplementary tools for annotating features obtained from SESI-MS. However, to increase confidence in the annotation results, combining these methods with additional on-line fragmentation techniques is recommended. This study presents a comparison between features detected in exhaled breath using SESI-HRMS and those identified in breath condensate. The breath condensate analysis involved the use of a headspace-sampling GC–MS method and a highly sensitive LC-MS2 acquisition approach. Approximately one-fourth of the features detected online exhibited corresponding matches in either the GC–MS or LC-MS analysis. [Display omitted] [ABSTRACT FROM AUTHOR]