LC/GC(×GC) A POWERFUL TOOLBOX IN FOOD ANALYSIS

The advent of hyphenated chromatographic techniques has profoundly reshaped our understanding of food matrices, revealing chemical complexity that often remain hidden with one-dimensional separations. This increasing analytical demand has driven the development of multidimensional chromatographic approaches aimed at enhancing separation capacity and analytical confidence. The coupling of liquid chromatography and gas chromatography (LC–GC) was first reported in 1980 as an elegant solution to combine orthogonal separation mechanisms. Despite its conceptual strength, LC–GC experienced a long and uneven evolution, never fully achieving the recognition it deserved and even facing periods of commercial withdrawal.
In contrast, comprehensive two-dimensional gas chromatography (GC×GC), introduced in 1991, rapidly attracted attention within the separation science community and found widespread application across environmental, petrochemical, and food analysis.
A renewed interest in LC–GC emerged in the late 2000s with the increasing concerns related to the determination of mineral oil hydrocarbons (MOH) in food. In this context, LC–GC established itself as the reference routine approach, enabling effective class separation prior to capillary GC analysis. The need of additional separation power has been evident to increase the accuracy and the information density obtained from the MOH analysis. A natural consequence has been to extend the dimensionality of LC-GC-FID to a fully integrated and hyphenated LC–GC×GC platforms coupled with time-of-flight mass spectrometry and flame ionization detection (TOFMS/FID).
These systems have demonstrated a level of robustness and reproducibility compatible with routine food analysis, while offering unprecedented separation power and qualitative insight. Beyond enhanced resolution, the LC–GC×GC approach provides highly informative chromatographic fingerprints and significantly reduces uncertainty throughout the analytical workflow. The exploitation of multiple dimension selectivities has been revealed to be useful for various applications allowing for a simplification of the sample preparation workflow, while increasing robustness and reliability of the analytical characterization.