Exploring food volatilome by advanced chromatographic fingerprinting based on comprehensive two-dimensional gas chromatographic patterns

The contribution provides proof-of-evidence on how effective are chromatographic fingerprinting methodologies based on comprehensive two-dimensional gas chromatographic data to explore the complex volatilome of food. In particular, hazelnut (Corylus avellana L.) volatilome is here taken as a test bench to demonstrate how the different kinds of features for chromatographic fingerprinting can provide access to a higher level of information. Chemical signatures of native volatiles in raw hazelnuts are explored by peak-features to study the impact of post-harvest and storage conditions. Datapoint features and peak-region features are used in a hybrid workflow to detect potent odorant patterns correlated to spoilage. Sensory-driven explorations, in the context of sensomics, enable the aroma blueprint definition whilst Artificial Intelligence smelling can be implemented by combining reliable quantification with automated headspace sampling and effective separation and detection by comprehensive 2D-GC.