Computer vision–based augmented visualisation for coffee origins identitation using comprehensive two-dimensional gas chromatography

Coffee is a highly complex and variable matrix, with volatile profiles shaped by multiple factors including botanical origin, climatic and soil conditions, post-harvest treatments, and roasting parameters. This variability generates complex chemical patterns, encompassing hundreds of volatile compounds from diverse chemical classes including pyrazines, furans, aldehydes, ketones, and terpenes. The resulting chemical dimensionality poses significant analytical challenges, making the accurate identification of characteristic compounds and reliable discrimination of coffee origins particularly difficult. In this study, we applied comprehensive two-dimensional gas chromatography (GC×GC) coupled with computer vision (CV) to address these challenges. The workflow begins with untargeted fingerprinting, capturing all detectable compounds in a feature template. Multiple sample chromatograms are then combined into composite class images, representing the typical chemical features of each origin while minimising individual variability, which enables rapid pairwise comparison of different origins. CV-based pairwise comparisons highlight differential peaks, which are integrated into a targeted template for subsequent peak extraction. Multivariate analyses then identify the key discriminant compounds driving origin differentiation. Post-processing strategies, such as ion-specific intensity mapping, further enhance interpretability, enabling visualisation of compositional differences across key chemical families. Overall, this GC×GC-CV workflow provides a robust, rapid, and visually intuitive platform for comprehensive chemical characterisation and origin classification of coffee, integrating untargeted and targeted analyses in a single framework.