Elimination of chromatographic and mass spectrometric problems in GC-MS analysis of Lavender essential oil by multivariate curve resolution techniques: Improving the peak purity assessment by variable size moving window-evolving factor analysis.

In analysis of complex natural matrices by gas chromatography-mass spectrometry (GC-MS), many disturbing factors such as baseline drift, spectral background, homoscedastic and heteroscedastic noise, peak shape deformation (non-Gaussian peaks), low S/N ratio and co-elution (overlapped and/or embedded peaks) lead the researchers to handle them to serve time, money and experimental efforts. This study aimed to improve the GC-MS analysis of complex natural matrices utilizing multivariate curve resolution (MCR) methods. In addition, to assess the peak purity of the two-dimensional data, a method called variable size moving window-evolving factor analysis (VSMW-EFA) is introduced and examined. The proposed methodology was applied to the GC-MS analysis of Iranian Lavender essential oil, which resulted in extending the number of identified constituents from 56 to 143 components. It was found that the most abundant constituents of the Iranian Lavender essential oil are α-pinene (16.51%), camphor (10.20%), 1,8-cineole (9.50%), bornyl acetate (8.11%) and camphene (6.50%). This indicates that the Iranian type Lavender contains a relatively high percentage of α-pinene. Comparison of different types of Lavender essential oils showed the composition similarity between Iranian and Italian (Sardinia Island) Lavenders.