Development of Mass Spectrometry based methods for bioactive molecule characterization

This doctoral thesis focuses on the development and application of mass spectrometry (MS)-based analytical workflows for the detection, characterization, and quantification of small bioactive molecules and potential molecular markers in complex matrices of environmental, microbial, plant, and human origin. Rather than targeting a single class of compounds or relying on a fixed instrumental platform, the approach prioritizes methodological flexibility, tailoring each step—from sample preparation to data acquisition and interpretation—to the specific chemical properties and biological context of interest. In the field of environmental toxicology, targeted and validated LC–MS/MS methods were developed for pesticides, ultra-short-chain per- and polyfluoroalkyl substances (USC-PFAS), and isoprostanes, with applications to real samples and international comparative surveys. For microbial interactions, targeted and semi-targeted metabolomics studies investigated metabolic shifts in single and mixed bacterial–fungal cultures, identifying key pathways related to oxidative stress and postbiotic signalling molecules. In the plant domain, LC–MS and MALDI–MSI workflows were applied to map functional and nutritionally relevant compounds, including purines in fermented soybean products, and polyphenols, amino acids and other secondary metabolites in wine by-product extracts. The integration of high- and low-resolution platforms, targeted and untargeted strategies, and spatial metabolomics enabled a comprehensive compositional and functional overview, supporting fundamental research, quality control and nutraceutical applications. The unifying element of this work is the adoption of a “fit-for-purpose” analytical strategy, in which the choice of extraction, separation, MS platform, and data processing pipeline is guided by the research question and the molecular nature of the analytes. The results demonstrate how mass spectrometry, when embedded within adaptable and robust workflows, can provide not only reliable quantitative data but also valuable interpretative insights into complex biological and ecotoxicological processes.