Are the “applications of the future” the key to unlocking the deserved success of GC×GC?

Modern “omics” strategies applied to food and nutrition domains require a “comprehensive” approach to capture the compositional complexity of samples (i.e., chemical code – metabolome, volatilome) and to establish robust correlations with the complex biological phenomena behind them. Comprehensive two-dimensional gas chromatography combined with mass spectrometry (GC×GC-MS) has the potential to tackle compositional challenges in terms of high chemical dimensionality and a large dynamic range of concentrations of food and biological samples. When implemented by artificial intelligence (AI) algorithms and concepts, it provides a consistent basis for hypothesis generation. Undoubtedly, GC×GC-MS is a key-analytical platform in food metabolomics/food volatilomics and its widespread has boosted research opening new and concrete perspectives for a more comprehensive understanding of food quality, nutritional value, and functional properties. The industrial request for straightforward analytical workflows and solutions to solve practical problems does not prevent the adoption of multidimensional chromatography platforms and omics concepts even in an industrial research or quality control laboratory framework. The larger the breadth of an investigation, the better the understanding of the impact of processing, storage, fermentation, and biotransformation on the overall quality and safety of a product. The full potential of GC×GC was probably not clear at its introduction, but its widespread adoption in different areas and the infusion of strategies and concepts from other disciplines, have definitely highlighted its central role of missing technique: “from a technique that did not exist… to a technique that was missing”1. The potential of GC×GC in food research will be presented from the perspective of the author's experience; future trajectories will also be delineated based on food industry requirements. Artificial Intelligence smelling, computer vision, food identitation, quality prediction, are all intriguing topics where GC×GC has no equal.