Magliulo, Raffaele (2026) Exploiting fermented foods microbiome to improve food quality and human health. [Tesi di dottorato]
|
Documento PDF
Magliulo_R_PhD_Thesis_UniNA_Food_Science_XXXVIII.pdf Visibile a [TBR] Amministratori dell'archivio Download (14MB) | Richiedi una copia |
|
|
Documento PDF
Magliulo_R_PhD_Thesis_UniNA_Food_Science_XXXVIII_Secreted.pdf Visibile a [TBR] Amministratori dell'archivio Download (7MB) | Richiedi una copia |
| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | Exploiting fermented foods microbiome to improve food quality and human health |
| Autori: | Autore Email Magliulo, Raffaele raffae.magliulo@gmail.com |
| Data: | 3 Febbraio 2026 |
| Numero di pagine: | 160 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Agraria |
| Dottorato: | Food Science |
| Ciclo di dottorato: | 38 |
| Coordinatore del Corso di dottorato: | nome email Barone, Amalia ambarone@unina.it |
| Tutor: | nome email De Filippis, Francesca [non definito] |
| Data: | 3 Febbraio 2026 |
| Numero di pagine: | 160 |
| Parole chiave: | Fermented foods, microbiome, terroir, health benefits |
| Settori scientifico-disciplinari del MIUR: | Area 07 - Scienze agrarie e veterinarie > AGR/16 - Microbiologia agraria |
| Informazioni aggiuntive: | Versione NON secretata della tesi. Ciclo 38. |
| Depositato il: | 17 Feb 2026 10:53 |
| Ultima modifica: | 12 Ago 2026 05:37 |
| URI: | https://www.fedoa.unina.it/id/eprint/16192 |
Abstract
Fermented foods are among the oldest biotechnologies used by humans, yet only recently we have started to examine them with the tools of contemporary microbiome science, multiomics, and artificial intelligence. This thesis sits at the intersection of food microbiology, bioinformatics, and data science. Its overarching aim is to characterise the microbiomes of fermented foods in a systematic, genome-resolved way, and to explore how these communities contribute to food quality, safety, and potential health-relevant functions. The work focuses primarily on traditional cheeses and kombucha, using shotgun metagenomics, complementary ‘omics, and explainable machine learning to move from descriptive catalogues of microbes towards predictive insights. The thesis is organised into five main chapters, followed by a general discussion that integrates the findings and outlines future directions. Each chapter builds on the previous ones, moving from a broad conceptual framework to detailed case studies, then to large-scale resources and finally to a potential model system for precision fermentation.
Downloads
Downloads per month over past year
Actions (login required)
![]() |
Modifica documento |


