This doctoral thesis investigated how multiple factors influence the chemical profile of Parmigiano Reggiano (PR) PDO milk and cheese by combining advanced analytical platforms with integrated chemometric and multi-omics approaches. Recent progress in metabolomics has expanded the application of metabolomics in dairy science, enabling a deeper understanding of the complex interactions that determine food quality and integrity. In PDO products such as PR cheese, key variables including ripening, cow diet, altitude of production, and farm management contribute to shaping the final chemical fingerprint. The first part of this work demonstrated the effectiveness of LC-HRMS metabolomics in capturing biochemical variations along the PR production chain, revealing molecular signatures associated with feeding strategies, ripening, and farm-level management. These findings confirmed metabolomics as a robust and discriminating tool for the compositional characterization of PR-derived products. The thesis also explored the integration of metabolomics with other omics disciplines, including metagenomics and lipidomics, to achieve a more comprehensive understanding of milk quality, authenticity, and traceability. This multi-omics strategy was applied to PR milk to investigate complex experimental factors such as milking time, spontaneous milk creaming, organic certification, and cow feeding strategies. By combining metabolomics with other omics technologies, the study highlighted the potential of integrated omics approaches to clarify the biochemical and microbiological mechanisms underlying milk processing and quality traits. Data-fusion strategies and multiblock models, including DIABLO and sPLS within the mixOmics framework, enabled the identification of biochemical networks, metabolic pathways, and microbial mechanisms that modulate milk quality and influence the nutritional properties of dairy matrices.
Nel contesto della valorizzazione e della caratterizzazione del Parmigiano Reggiano DOP, questa tesi di dottorato approfondisce il ruolo di molteplici fattori nella definizione del profilo chimico del latte e del formaggio attraverso l’impiego di tecniche analitiche avanzate e approcci chemometrici integrati. I progressi nel campo della metabolomica ne hanno favorito l’applicazione anche nell’ambito delle scienze lattiero-casearie. In matrici alimentari complesse come il Parmigiano Reggiano, la qualità e l’integrità del prodotto dipendono infatti dall’interazione di numerosi fattori, tra cui la stagionatura, l’alimentazione bovina e l’altitudine di produzione, rendendo necessari strumenti avanzati per la gestione, l’elaborazione e l’interpretazione dei dati. Gli studi presentati in questo lavoro di tesi hanno dimostrato l’elevata efficacia della metabolomica nel rilevare le variazioni biochimiche lungo la filiera produttiva del Parmigiano Reggiano. Nell’ambito del disciplinare di produzione DOP, l’approccio di metabolomica untargeted mediante UHPLC-HRMS si è rivelato uno strumento affidabile e robusto per identificare fingerprint molecolari associati a differenti strategie alimentari, al grado di stagionatura e a fattori correlati all’autenticità del formaggio grattugiato. La tesi ha inoltre evidenziato il valore dell’integrazione tra metabolomica e altre tecnologie omiche, quali metagenomica e lipidomica, per approfondire gli aspetti relativi alla qualità, all’autenticità e alla tracciabilità del latte. In particolare, l’approccio multi-omico ha consentito di analizzare fattori connessi alle condizioni di lavorazione del latte crudo, come il momento della mungitura e l’affioramento naturale in caldaia, nonché la loro interazione con variabili consolidate, tra cui la certificazione biologica e differenti strategie alimentari seguite delle bovine. L’integrazione dei dati omici, mediante strategie di data fusion, ha consentito di identificare network biochimici, pathway metabolici e meccanismi microbici coinvolti nella modulazione della qualità del latte e delle proprietà nutrizionali della matrice finale.
Becchi, Pier Paolo, Unravelling the quality and authenticity traits in Parmigiano Reggiano PDO products through integrated analytical approaches based on multiomics and machine learning, Lucini, Luigi, Zhang, Leilei, Università Cattolica del Sacro Cuore SEDE DI PIACENZA:Ciclo XXXVIII [https://hdl.handle.net/10807/332417]
Unravelling the quality and authenticity traits in Parmigiano Reggiano PDO products through integrated analytical approaches based on multiomics and machine learning
Becchi, Pier Paolo
2026
Abstract
This doctoral thesis investigated how multiple factors influence the chemical profile of Parmigiano Reggiano (PR) PDO milk and cheese by combining advanced analytical platforms with integrated chemometric and multi-omics approaches. Recent progress in metabolomics has expanded the application of metabolomics in dairy science, enabling a deeper understanding of the complex interactions that determine food quality and integrity. In PDO products such as PR cheese, key variables including ripening, cow diet, altitude of production, and farm management contribute to shaping the final chemical fingerprint. The first part of this work demonstrated the effectiveness of LC-HRMS metabolomics in capturing biochemical variations along the PR production chain, revealing molecular signatures associated with feeding strategies, ripening, and farm-level management. These findings confirmed metabolomics as a robust and discriminating tool for the compositional characterization of PR-derived products. The thesis also explored the integration of metabolomics with other omics disciplines, including metagenomics and lipidomics, to achieve a more comprehensive understanding of milk quality, authenticity, and traceability. This multi-omics strategy was applied to PR milk to investigate complex experimental factors such as milking time, spontaneous milk creaming, organic certification, and cow feeding strategies. By combining metabolomics with other omics technologies, the study highlighted the potential of integrated omics approaches to clarify the biochemical and microbiological mechanisms underlying milk processing and quality traits. Data-fusion strategies and multiblock models, including DIABLO and sPLS within the mixOmics framework, enabled the identification of biochemical networks, metabolic pathways, and microbial mechanisms that modulate milk quality and influence the nutritional properties of dairy matrices.| File | Dimensione | Formato | |
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