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The contribution focuses on untargeted data processing/analysis approaches that are currently adopted to explore the 4D-data matrices produced by comprehensive two-dimensional gas chromatography-mass spectrometry (GC × GC-MS) in food-omics. Strategies for untargeted explorations are rationalized through the type of features adopted (i.e., visual, datapoint, peak, and peak-regions) at the data processing level, and then discussed through relevant applications and illustrative examples, selected over peer-reviewed literature. The role of MS, including high vs. low resolution MS, as an active probe for specific cross-comparative analysis, is critically discussed also in the context of spectral deconvolution and subtraction, well-established procedures for 1D GC-MS explorations. Moreover, the challenging task of post-targeting aimed at identifying “unknown – knowns”, is examined in its potential, being the key to access a higher level of information. Selected examples emphasize the importance of reliable identification by retention indexing, retention pattern ordering, sensory evaluation (sensory analysis and olfactometry), and data mining.
•GC × GC-MS for effective untargeted exploration in food-omics.•MS as fundamental dimension to improve untargeted cross-comparative analysis.•Synergy of spectral deconvolution/subtraction and peak-regions pattern recognition.•Effective post-targeting of “unknown – knowns” aided by ordered retention patterns.•Exploring multiple analytical dimensions of GC × GC-MS by features approaches.