Online volatilomics of food fermentation by PTR-ToF-MS for sustainable bioprocess innovation

This thesis explores Direct Injection Mass Spectrometry techniques for studying fermentation processes in the food sector through volatilomics. While current metabolomics research mainly targets non-volatile organic metabolites, volatile organic compounds (VOCs) are often underestimated despite their potential to deliver rapid, non-destructive, and real-time results. This work particularly examines the use of Proton Transfer Reaction Time-of-Flight Mass Spectrometry (PTR-ToF-MS) as a fast, direct, and highly sensitive platform for monitoring VOCs in food fermentation processes in real-time. VOCs are more than simple microbial metabolism by-products; they serve as valuable markers for understanding microbial systems and food quality, capturing dynamic traits that can help establish volatilomics as a tool for sustainable food bioprocess innovation. Gas Chromatography-Mass Spectrometry (GC-MS) is the standard method for VOC identification and quantification; however, it involves an extraction step, making it time-consuming and destructive, and less suitable for large-scale screening or real-time online monitoring. As reducing analysis time and tracking microbial metabolism become priorities in the fermentation industry, there is a need to utilise high-sensitivity techniques such as PTR-ToF-MS. Recognised as a non-invasive, rapid monitoring system, PTR-ToF-MS can be used both for screening and for real-time online analysis. Combining GC-MS and PTR-ToF-MS within a single experimental setup leverages their respective strengths, identification capability and high temporal resolution, while supporting the development of a more eco-friendly analytical approach. To validate this integrated approach, the experimental framework was organised across three levels of increasing complexity. The first level concentrated on the metabolic characterisation of individual strains, utilising Lactiplantibacillus plantarum as a model species due to its nomadic lifestyle and notable metabolic versatility in plant-based matrices. This includes strain WCFS1, one of the best studied lactobacilli. Using online monitoring under optimal conditions with a standard microbiological medium, the system successfully distinguished different L. plantarum strains based on specific volatile fingerprints, which are putatively linked to amino acid degradation, fatty acid oxidation, and stress response. The second level applied this approach to real, complex matrices by monitoring VOCs in plant-based milk-like beverages (i.e., soy, oat, and almond), using bovine milk as an animal-origin reference, characterising the raw materials, and evaluating the effects of fermentation with L. plantarum WCFS1. Results showed that the integrated platform effectively tracked the production of key metabolites and desirable yoghurt-like compounds, while concurrently monitoring the reduction of off-flavours, thereby providing a dynamic understanding of substrate-specific pathways. Lastly, the third level extended the methodology to complex microbiomes, using the fermentation of cv. Bella di Cerignola PDO table olives as a case study. The integration of PTR-ToF-MS and GC-MS enabled clear discrimination between ‘natural’ and ‘spanish’ fermentation styles, revealing that spontaneous ‘natural’ fermentation produces a significantly richer and more diverse volatilome, characterised by markers of active alcoholic, heterolactic, and propionic fermentations. Other case studies complement the thesis, broadening the research scope and enhancing the assessment of the potential of the proposed analytical strategy. In conclusion, the use of PTR-ToF-MS as a high-sensitivity volatilome sensor, supported by GC-MS identification, proved to be a robust analytical platform for detecting volatile markers linked to microbial metabolism. This approach enables quick differentiation and selection of microbial starters and offers valuable insights for optimising and controlling fermentation processes. Furthermore, the proposed analytical framework promotes innovation in the fermented food sector and aligns with Green Analytical Chemistry (GAC) principles by reducing analysis time and solvent use, thereby opening new opportunities for real-time monitoring of complex food ecosystems.