INVESTIGATING QUALITY TRAITS IN ARTISANAL CHEESE BY COMPREHENSIVE TWO-DIMENSIONAL GAS CHROMATOGRAPHY AND QUANTITATIVE VOLATILOMICS

Assessing food quality is essential for consumer satisfaction and safety, yet traditional analytical methods often fall short in capturing the complex molecular and biochemical interactions within food matrices like cheese. This study utilizes advanced analytical techniques and Artificial Intelligence (AI) tools, such as Computer Vision (CV), to explore these intricate molecular interactions during artisanal cheese ripening. The research focuses on the benefits of comprehensive two-dimensional gas chromatography coupled with mass spectrometry and flame ionization detector (GC×GC-MS/FID), which offers superior resolution and sensitivity compared to conventional one-dimensional GC. Moreover, by image patter recognition algorithms that track and align features over many patterns, CV could be featured providing a prompt evidence of compositional differences among samples classes. Focusing on Valcasotto cheese, a Traditional Food Product, the sampling covered the entire production chain, including milk from two farms and harvest seasons (spring and summer), with curds ripened in controlled environments and Valcasotto caves for 30, 90, and 120 days. Utilizing multiple headspace solid phase microextraction (MHS-SPME), we optimized the capture of a wide range of volatiles and semi-volatiles produced during cheese production. Quantitative volatilomics precisely tracked marker volatiles and impactful odorants, including key-aroma compounds, across samples, identifying markers that define the unique traits of Valcasotto cheese. Significant compounds such as acetoin, phenylethyl alcohol, and sulfur derivatives were identified, highlighting the potential of GC×GC-MS in food quality assessment and flavor analysis.