Abstract: In this study, we analyzed the volatile organic components of sunflower oils with different degrees of refining by using gas chromatography-ion mobility spectrometry (GC-IMS) and we constructed fingerprints. Our aim was to discriminate and classify the sunflower oils based on refining degree. A total of 22 effective characteristic peaks were selected by two-dimensional differential spectrometry as variables to characterize the changes in refining degree. Principal component analysis (PCA) and supervised pattern recognition methods such as the k-nearest neighbor (kNN) method, quadratic discriminant analysis (QDA), and Fisher linear discriminant analysis (FLDA) were used to carry out discriminant analysis. The results showed that all three supervised pattern recognition methods could effectively distinguish the sunflower oil samples with an accuracy rate of up to 97.30%. GC-IMS combined with chemometrics could accurately and visually distinguish sunflower oil samples with different refining degrees. This method facilitates the quality control of vegetable oils in enterprises and provides a new detection method for quality inspection technicians.

Key words: sunflower oil, volatile organic compounds, refining degree, gas chromatography-ion mobility spectrometry