Integrated analytical and data-driven approaches for food integrity, traceability and agri-food innovation

Food integrity has emerged as one of the major scientific challenges for contemporary agri-food systems, requiring multidisciplinary approaches capable of integrating food authenticity, traceability, sustainability, and transparency within the broader One Health paradigm. Rather than being defined by isolated analytical markers, food integrity reflects the complex interactions among environmental conditions, agricultural practices, biological processes, and technological factors that collectively shape the identity of agricultural products. Addressing this complexity requires innovative analytical methodologies and integrated strategies that combine complementary chemical, biological, and digital information into robust and interpretable frameworks for food integrity assessment. To address these challenges, this PhD research aimed to advance food integrity through the development of innovative analytical methodologies and the integration of complementary analytical, biological, and digital approaches for food authenticity, geographical traceability, and sustainable agri-food systems. The research integrates expertise in analytical chemistry, isotope ratio analysis, microbiome science, metabolomics, chemometrics, explainable artificial intelligence, and FAIR-oriented data management to generate robust, reproducible, and biologically meaningful information for the characterization of complex agri-food systems. Beyond its experimental contributions, the research also addresses the methodological foundations of next-generation food integrity systems by integrating metrology, FAIR principles, interoperable digital infrastructures, and trustworthy artificial intelligence to improve the quality, transparency, and reproducibility of analytical information throughout the agri-food chain. This multidisciplinary approach was progressively developed through complementary case studies spanning different agri-food matrices and levels of biological organization. Wheat production systems were employed as a model to investigate the relationships linking geological background, soil properties, agricultural management, plant genotype, and food composition through the integration of multi-elemental and isotopic fingerprinting, microbiome-informed system characterization, metabolomic profiling, and explainable machine learning. This systems-level approach demonstrated that combining complementary analytical descriptors not only improves geographical authentication and traceability but also provides mechanistic insights into the ecological and biological processes underlying food identity. In particular, the integration of elemental and isotopic signatures with microbiome- and metabolome-related information enabled a more comprehensive understanding of the soil–plant–food continuum and of the factors contributing to product differentiation and provenance. The methodological framework was subsequently extended to extra virgin olive oil, where a direct dilute-and-shoot ICP–AES method was developed and analytically validated for rapid multi-element determination, significantly simplifying sample preparation while maintaining analytical reliability. Combined with stable isotope analysis, this methodological advance establishes the basis for an integrated analytical strategy supporting authenticity assessment and geographical traceability of Mediterranean olive oils. Finally, integrated elemental and metabolomic approaches were applied to Coffea arabica leaf-derived cell cultures, supporting the characterization of metabolic reprogramming and the development of innovative functional ingredients. Beyond authenticity applications, this case study demonstrated the broader potential of advanced analytical characterization for the valorization of biological resources and the development of innovative food-related products. Collectively, these findings support a shift from viewing food authentication as the identification of isolated analytical markers to understanding food integrity as an emergent property of interconnected agri-food systems. The results further highlight how food identity emerges from the interaction of environmental, biological, chemical, and digital dimensions, reinforcing the need for integrated and systems-oriented approaches to agri-food research. The integration of advanced analytical chemistry, systems biology, artificial intelligence, and interoperable data ecosystems within the One Health paradigm establishes a conceptual and methodological framework for the next generation of sustainable, transparent, and resilient agri-food systems.