Geographical Differentiation of Southern Italian and Spanish Bean Ecotypes Through Integrated GC-HRMS Based Metabolomic and Chemometric Approaches

The geographical origin of pulses influences their chemical composition and functional properties, due to environmental and agronomic factors. In this study, gas chromatography coupled with Q-Orbitrap-high-resolution mass spectrometry (GC-Q-Orbitrap-HRMS) was applied to explore compositional differences between Spanish and Southern Italian bean samples using a non-targeted metabolomic approach. Volatile compounds were profiled by headspace solid-phase microextraction (HS-SPME), while non-polar semi-volatile components were investigated using salt-assisted liquid-liquid extraction (SALLE) followed by direct injection. Multivariate statistical analyses, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), revealed partial group structuring under unsupervised conditions and satisfactory supervised model performance within the investigated dataset (R2Y > 0.8; Q2 > 0.6). An exploratory mid-level data fusion strategy was implemented to integrate complementary analytical features from both datasets and examine its effect on model performance and variable ranking. Overall, seven metabolites (e.g., 1-hexadecanol, 3,5-octadien-2-one, hexadecane) were putatively annotated in the fused dataset. They contributed to the supervised differentiation of Southern Italian and Spanish beans, highlighting origin-related variability within the metabolomic fingerprint of geographically related legume matrices.