FERRARA, GIOVANNI (2025) Mass spectrometry-based identification of volatile markers during goat cheese ripening: perspectives in biosensor design. [Tesi di dottorato]

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Abstract

This work analyzes the evolution of volatile organic compounds (VOCs) during goat cheese ripening with the aim of developing a conceptual design for a biosensor capable of monitoring ripening status, using marker compounds as targets. Cheese samples were analyzed at 0, 30, 60, 90, 120, and 150 days of ripening using Headspace Solid-Phase Microextraction (HS-SPME) coupled to Gas Chromatography–Mass Spectrometry (GC-MS) for the identification and quantification of VOCs. Throughout the entire investigation, 68 volatile compounds belonging to different chemical classes (alcohols, ketones, aldehydes, esters, carboxylic acids, terpenes, sulfur compounds, and others) were identified, highlighting how VOC dynamics showed a general increase in aromatic complexity up to 120 days, with a partial decrease at day 150. Carboxylic acids were the predominant class in the advanced stages of maturation, while aldehydes and esters characterized the initial stages. Ketones and some secondary alcohols such as 2-butanone and 2-butanol were strongly associated with the final stages of maturation, with a significant increase on the last day of maturation. This association was confirmed by multivariate PCA statistical analysis, which also allowed the identification of a subset of VOCs that discriminate between the early maturation stage and the advanced maturation stage. Based on the chemical and analytical results, a VOC-based biosensor concept was proposed, exploring different molecular recognition elements (MREs) as potential candidates for the selective detection of 2-butanone and 2-butanol. The selection of these MREs was supported by in silico molecular docking simulations, which are being used to investigate their potential affinity and suitability as recognition components for future biosensor development. In parallel, analysis of the raw material, represented by goat's milk collected at the Accadia Verde production farm, where the industrial part of this doctoral project was carried out, highlighted seasonal variations in physical, chemical, and microbiological parameters, influenced by climatic conditions and company management, highlighting the importance of milk quality as a fundamental prerequisite for dairy production. The work therefore integrates laboratory analytical approaches and molecular modeling to provide useful tools for quality control. The results offer both fundamental knowledge of the biochemical pathways that generate aroma in goat cheeses and applied control for the experimental development of a sensor that could enable non-destructive, rapid, and continuous monitoring in the production environment.

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