Beyond academic performance: enabling GC×GC-MS for the constraints of the real lab

Advances in separation science are most impactful when they translate into robust, efficient, and realistically applicable analytical workflows. In the context of food quality and authenticity, comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC×GC-MS) represents a powerful platform to investigate complex volatile and semi-volatile fractions. However, its full potential is often constrained by data dimensionality, the lack of streamlined data interpretation pipelines, and the gap between method complexity and industrial feasibility. This contribution focuses on the strategic development of GC×GC-MS workflows aimed at maximizing information return while minimizing unnecessary analytical overhead. Emphasis is placed on the practical implementation of structured workflows for chemical fingerprinting, selective spectral mining, and marker discovery, with particular attention to their scalability and reproducibility in routine settings. Case studies from food volatilomics and sensomics illustrate how advanced chromatographic data, when interpreted through optimized processing strategies, can support the identification of authenticity markers and sensory-relevant compounds, even under conditions of co-elution or matrix interferences. Rather than pursuing exhaustive data exploration or "exotic" methodological solutions, this work underscores the importance of methodological restraint and purpose-driven design in multidimensional analysis. The goal is to enable separation strategies that are not only chemically comprehensive, but also operationally viable and aligned with the constraints of current industrial laboratories.