This academic article explores the potential of gas chromatography (GC) to predict the origin and vintage of Bordeaux red wines. Researchers employed machine learning (ML) techniques on raw gas chromatograms from numerous wines across seven estates and twelve vintages. They discovered that nonlinear dimensionality reduction could surprisingly map the geography of the Bordeaux region based on the wine's chemical profile. The study achieved near-perfect accuracy in identifying the wine's estate and up to 50% accuracy in predicting the vintage, suggesting that a wine's chemical identity is distributed across a broad spectrum of molecules rather than just a few key compounds. This approach offers a more affordable and less labor-intensive method than traditional targeted analysis, highlighting GC's remarkable utility in wine science.