The analysis of complex volatile mixtures by gas chromatography-mass spectrometry (GC-MS) is a time-consuming process. It involves separation and identification of the components based on their retention times and fragmentation patterns, followed by determination of their relative percentages from integration of their peak areas. Herein we show that multivariate statistical analysis of the relative abundances of the m/z values obtained from the average mass scans (AMS) of the complex mixture is a faster and potentially more reliable method of assessing these mixtures. To achieve this, 15 model complex mixtures, were prepared comprising varying amounts of 10 different constituents. The AMS profile and chemical composition of each mixture were compared to one another using agglomerative hierarchical cluster analysis and principal component analysis. The results obtained strongly suggest that multivariate statistical analysis of AMS profiles is a promising, time saving and reliable tool for analyzing complex volatile mixtures, in particular essential oils.
gas chromatography-mass spectrometry; multivariate statistical analysis; complex mixtures; average mass scan; percentage composition; total ion chromatogram
