MULTIVARIATE STATISTICAL TREATMENT OF PLANT EXTRACT COMPOSITIONAL DATA: AVERAGE MASS SCAN OF THE TOTAL ION CHROMATOGRAM (AMS) APPROACH

Abstract

It was recently confirmed that relative abundances of m/z values of the average mass scan of the total GC chromatograms (AMS) are suitable variables for multivariate statistical comparison (MVA) of essential oils. These are even more applicable, reliable and faster than the traditionally used variablespercentages (peak areas) of individual oil constituents. Herein, we have explored if AMS-derived variables are appropriate for MVA comparison of plant solvent extract compositional data. To achieve this, average mass scans of the total GC chromatograms and chemical compositions (relative percentages) of eight diethyl ether extracts (six different species; samples were analyzed using GC-FID and GC-MS; data from the literature) were separately compared using two MVA methods: agglomerative hierarchical clustering analysis and principal component analysis. The obtained results strongly suggest that MVA of complex volatile mixtures (GC-MS analyzable fractions of plant solvent extracts), using the corresponding AMS, could be considered as a promising time saving tool for easy and reliable comparison purposes. The AMS approach gives comparable or even better results than the traditional method.