Comprehensive two-dimensional gas chromatography a gestalt technique in food metabolomics

Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful analytical platform for effective investigations in the food-omic domain [1]. It combines the information capacity of profiling with the flexibility of fingerprinting strategies. Compared to mono-dimensional (1D)-GC, the comprehensive combination of two separation dimensions results in analytical platforms with remarkable separation power and enhanced sensitivity. In addition, the retention logic for structurally correlated components, generates 2D patterns that are distinctive sample fingerprints. As a gestalt “…a configuration or pattern of elements so unified as a whole that it cannot be described merely as a sum of its parts…”, GC×GC offers a perspective on samples’s compositional complexity that is particularly useful in challenging situations [2]. The contribution discusses, through applications developed in the author’s laboratory, the gestalt attitudes of GC×GC in food metabolomics research. Within the complex volatilome of high-quality hazelnuts it will be presented fingerprinting strategies to detect spoilage patterns and to define robust markers of storage quality. By the characteristic profile of volatiles it will be shown identitation capabilities of 2D patterns generated by GC×GC- TOFMS [3,4]. As a straightforward example, the potentials of Artificial Intelligence (AI) smelling based on sensomic principles [5], will be discussed in the perspective of AI decision-making tools for food quality in an industrial perspective.