GREEN(ER) Transition in Analytical Measurements for GREEN(NES) FOOD

This doctoral thesis focuses on the development of innovative and sustainable analytical methodologies for food quality and safety assessment, with a strong emphasis on the transition to greener practices. The research integrates Green Analytical Chemistry (GAC) principles, advanced chromatographic techniques, and artificial intelligence (AI)-based systems to address the growing demand for efficient, cost-effective, and environmentally friendly analytical solutions in food quality control. Several key areas of analytical chemistry were explored, primarily focusing on the development of fast, high-throughput methods for food analysis. An increase of efficiency and speed of analytical workflows were achieved by optimizing chromatographic techniques such as fast gas chromatography (fast-GC), while maintaining accuracy and reliability. These advancements are particularly relevant for food quality control, where rapid analysis is essential for meeting industrial demands. The analysis of large numbers of samples in a shorter time frame, supporting both sustainability and operational efficiency in food analysis was achieved thanks to the implementation of high-throughput methods; the development of portable GC systems was explored and preliminarily tested enhancing the accessibility and sustainability of food analysis by on-site systems. The ultimate goal, as a part of the EVOQUE project, is the development of a portable, compact GC-QEPAS (Quartz-Enhanced PhotoAcoustic Spectroscopy) system for volatile organic compounds (VOCs) detection with confirmatory attitudes as mass spectrometry (MS) but lower operational costs and background requirements. Comprehensive two-dimensional gas chromatography (GC×GC) coupled with MS detection, a technique that enhances separation capacity and provides detailed insights into complex food fractions, was also considered. This high-information capacity platform, when combined with AI tools like machine learning and computer vision, allows for the efficient analysis of large, complex datasets, improving food quality assessments and enabling more confident traceability, and multi-level quality characterization. Four applications were developed exploiting GC×GC-MS full potential; they include hazelnut quality leveling, butter processing characterization, maize silage VOCs quantification, and extra virgin olive oil (EVOO) aroma blueprinting. By identifying key volatile compounds (i.e., markers) and correlating them with quality or authenticity traits, the research offers valuable tools for shelf life quality predictions and stability, process monitoring during the industrial production chain, spoilage detection, and the identification of products’ origins. The integration of AI-driven chemometric models further enhances the reliability and precision of these analyses, supporting decision-making in food production and quality control. 12 | P a g e The development of greener methods for detecting contaminant VOCs was another key aspect of the research project. The use of renewable gases in GC workflows, such as hydrogen and nitrogen, alongside the optimization of purge-and-trap techniques, demonstrated how sustainability can be incorporated into VOC analysis without compromising regulatory compliance or performance standards. Overall, this thesis provides a comprehensive framework for modernizing food analysis by combining advanced chromatographic techniques, AI, and sustainable practices. The findings contribute to the advancement of green analytical chemistry, offering innovative solutions for improving food quality control, supporting sustainability, and advancing the green transition in analytical methodologies.