For the first time, a non-targeted metabolomics approach by means of ultraperformance liquid chromatography coupled to electrospray quadruple time-of-flight mass spectrometry was chosen for the discrimination of geographical origins of white asparagus samples (Asparagus officinalis). Over a period of four harvesting periods (4 years), approximately 400 asparagus samples were measured. Initially, four different liquid chromatography–mass spectrometry methods were used to detect as many metabolites as possible and to assess which method is most suitable. The most relevant marker compounds were linked to the influence of different plant stress parameters and climate effects. Some of the samples were also analyzed by isotope-ratio mass spectrometry (IRMS), which is the current gold standard for the discrimination of the geographical origin of asparagus. In summary, the analysis of the metabolome was proven to be quite suitable to determine the geographical origin of asparagus and seems to provide better interpretable results than IRMS studies.

The extraction of metabolites turns out to be one of the most important key factors for nontargeted metabolomics approaches as this step can significantly affects the informative value of the successive measurements. Compared to metabolomics experiments of various matrices of bacterial or mammalian origins, there are only few studies, which focus on different extraction methods for plant metabolomics analyses. In this study, various solvent extraction compositions were compared and assessed using an UPLC-ESI-QTOF-MS strategy. Exemplary, white asparagus (Asparagus officinalis) were employed as a low-fat-, low-protein-, high-water-content model commodity with the objective of designing an optimal nontargeted extraction protocol for polar and nonpolar metabolites. Furthermore, the influence of acid addition, mechanical cell disruption methods (ball mill, ultrasonic bath, vortex mixer), and extract stability have been systematically scrutinized too. The different extraction protocols were compared based on sum of features, sum of peak intensities, sum of peak areas, as well as by analyzing individual signals of as many different substance groups as possible to obtain a maximum overview.