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Mass spectrometry is an important analytical technology in metabolomics. After the initial feature detection and alignment steps, the raw data processing results in a high-dimensional data matrix ofmass spectral features, which is then subjected to furtherstatistical analysis. Univariate tests like Student's-t test and Analysis of Variances(ANOVA) are hypothesis tests which aim to detect differences betweentwo or more sample classes, e.g. wildtype-mutant or between different doses of treatments. In both cases, one of the underlying assumptions is the independence between metabolic features.However, in mass spectrometry a single metabolite usually gives riseto several mass spectral features, which are observed together and show a common behaviour.This paper suggests to group related features of metabolites with CAMERA into compound spectra, and then use a multivariate statistical method to test whether a compound spectrum (and thus the actual metabolite) is differential between two sample classes. The multivariate method is first demonstrated with an analysis between wild-type and an over-expression line of the model plant Arabidopsis thaliana. For a quantitative evaluation data sets with a simulated known effect between two sample classes were analysed. The spectra-wise analysis showed better detection results for all simulated effects.