Discrimination of different edible vegetable oils based on GC-IMS and SIMCA

Ion mobility spectrometry coupled to gas-chromatographic (GC-IMS) is tested regarding their ability to analyze various refined edible vegetable oils, including sunflower seed, rapeseed, sesame, soybean, peanut, corn, camellia, linseed, walnut, coconut, grape seed and extra virgin olive oils. GC-IMS assay displays peak difference of each edible vegetable oil in three-dimensional information at retention time in gas phase and at ion mobility rate in IMS. Moreover, 74 main peak intensities are extracted and imported into Excel for analysis. Then, chemometric methods are employed to establish discriminant models. The results show that based on Kennards-Stone (KS), the prediction accuracy of the soft independent modeling of class analogy (SIMCA) is perfect. Therefore, the GC-IMS system is shown to be an effective method to classification of edible vegetable oils.