New trends in crop breeding include analytical approaches to identify metabolic fingerprints that can be used for associations to the genetic background. The biochemical phenotype, as a result of plant endogenous factors and interaction with the environment, has the potential to increase the accuracy of forecasting regarding agronomical quality factors. In this study a metabolite profile analysis by gas chromatography–mass spectrometry (GC–MS) was conducted on sets of seed material from sugar beet. One set represented high-performing varieties with a close genetic background and with a similar quality in terms of germination capacity. The second set contained seed lots from different genotypes comprising different germination capacities. By multivariate statistical analyses high variance in both sample sets was revealed. These data were further allocated to corresponding metabolite classes. It could be shown that an untargeted GC–MS approach has the power to resolve differences in the molecular phenotypes of related offspring lines. Metabolic profiles were found to correlate more to genotypic differences than to differences in the germination capacity.

Bis(cyclopentadienyl)magnesium (MgCp2) is used commonly as a source for doping nitride materials with magnesium. Increased oxygen incorporation known to accompany the use of MgCp2 makes the purity of this precursor an important consideration in nitride CVD. Gas chromatography-mass spectroscopy (GCMS) methods have now been developed for the identification of volatile impurities in MgCp2. Diethylether, an oxygen containing organic compound (CH3CH2OCH2CH3), and additional organic impurities were found in the MgCp2 supplied by three manufacturers. Subsequent refinements in the synthetic processes by these companies have resulted in the availability of MgCp2 free of ether and other organic impurities as determined by GCMS.