Metabolomics profiling of human exhaled breath condensate by SPME/GC × GC-ToFMS: Exploratory study on the use of face masks at the level of lipid peroxidation volatile markers

Real-time breath analysis towards a healthy human breath profile

The direct analysis of molecules contained within human breath has had significant implications for clinical and diagnostic applications in recent decades. However, attempts to compare one study to another or to reproduce previous work are hampered by: variability between sampling methodologies, human phenotypic variability, complex interactions between compounds within breath, and confounding signals from comorbidities. Towards this end, we have endeavored to create an averaged healthy human 'profile' against which follow-on studies might be compared. Through the use of direct secondary electrospray ionization combined with a high-resolution mass spectrometry and in-house bioinformatics pipeline, we seek to curate an average healthy human profile for breath and use this model to distinguish differences inter- and intra-day for human volunteers. Breath samples were significantly different in PERMANOVA analysis and ANOSIM analysis based on Time of Day, Participant ID, Date of Sample, Sex of Participant, and Age of Participant (p< 0.001). Optimal binning analysis identify strong associations between specific features and variables. These include 227 breath features identified as unique identifiers for 28 of the 31 participants. Four signals were identified to be strongly associated with female participants and one with male participants. A total of 37 signals were identified to be strongly associated with the time-of-day samples were taken. Threshold indicator taxa analysis indicated a shift in significant breath features across the age gradient of participants with peak disruption of breath metabolites occurring at around age 32. Forty-eight features were identified after filtering from which a healthy human breath profile for all participants was created.

Chromatographic-Based Platforms as New Avenues for Scientific Progress and Sustainability

Chromatography was born approximately one century ago and has undergone outstanding technological improvements in innovation, research, and development since then that has made it fundamental to advances in knowledge at different levels, with a relevant impact on the well-being and health of individuals. Chromatography boosted a comprehensive and deeper understanding of the complexity and diversity of human–environment interactions and systems, how these interactions affect our life, and the several societal challenges we are currently facing, namely those related to the sustainability of our planet and the future generations. From the life sciences, which allowed us to identify endogenous metabolites relevant to disease mechanisms, to the OMICS field, nanotechnology, clinical and forensic analysis, drug discovery, environment, and “foodprint”, among others, the wide range of applications of today’s chromatographic techniques is impressive. This is fueled by a great variability of powerful chromatographic instruments currently available, with very high sensitivity, resolution, and identification capacity, that provide a strong basis for an analytical platform able to support the challenging demands of the postgenomic and post COVID-19 eras. Within this context, this review aims to address the great utility of chromatography in helping to cope with several societal-based challenges, such as the characterization of disease and/or physiological status, and the response to current agri-food industry challenges of food safety and sustainability, or the monitoring of environmental contamination. These are increasingly important challenges considering the climate changes, the tons of food waste produced every day, and the exponential growth of the human population. In this context, the principles governing the separation mechanisms in chromatography as well the different types and chromatographic techniques will be described. In addition, the major achievements and the most important technological advances will be also highlighted. Finally, a set of studies was selected in order to evince the importance of different chromatographic analyses to understand processes or create fundamental information in the response to current societal challenges.

Advanced tuning of the ion management parameters in GC × GC-HRMS using a Fourier transform Orbitrap mass analyzer for pixel-based data handling and multivariate analysis

GC × GC investigations are well known to generate a substantial amount of information-rich and structurally complex data, requiring advanced data processing strategies like chemometrics. Many workflows are available for data handling and processing, such as the peak-table and pixel-based approaches. The goal of this work is to present a solution based on method development to solve the missing pixel problem that may be encountered in experiments performed with GC and GC × GC coupled to the Fourier transform orbital ion trap (FT-Orbitrap) mass analyzer. Data input is vital for pixel-based chemometric analyses, as some post-processing solutions may lead to significant loss of chemical information in the data set. Hence, a key requisite is that the chemical information is consistently indexed in the data arrays for proper pixel-based data handling and analysis. In this study, we carefully evaluated the ion management parameters to preserve the intrinsic structure and information of the data arrays of the GC × GC-FT-Orbitrap for future pixel-oriented chemometric analysis. The most acceptable conditions yielded acquisition rates up to 42.6 spectra per s, while a routine setting of 24.7 Hz was successfully employed in analyses of different petroleum fractions, producing both consistent tensor sizes and acceptable peak reconstructions. A data acquisition rate of 24.7 spectra per s and a mass resolving power of 15 000 allowed the resolution of a mass split of only 0.004 Da - which is an interesting configuration for challenging applications in petroleomics. Using such advanced settings, the missing pixel problem was reduced from up to 30% to much less than 0.04% of the data array dimension. Thus, the proposed configuration can be employed in studies that require pixel-oriented multivariate data analysis.