Discriminating geographic origin of sesame oils and determining lignans by near-infrared spectroscopy combined with chemometric methods

Near-infrared spectroscopy (NIRS) combined with chemometric methods were used to discriminate the sesame oils from different Chinese provinces and determinate the lignans (sesamin and sesamolin) in these sesame oils. The geographic discriminant model was constructed by principal component analysis (PCA) combining with linear discriminant analysis (LDA), and the quantitative analysis models of lignans were built using partial least squares (PLS) regression. Multiplicative scatter correction (MSC) and competitive adaptive reweighted sampling (CARS) were adopted to optimize the regression models. It was found that the discriminant model could recognize the sesame oils from different Chinese provinces correctly, and the contents of sesamin and sesamolin calculated from the optimized models and high-performance liquid chromatography (HPLC) analysis are rather close. Reasonable results proved the feasibility of near-infrared spectroscopy (NIRS) combined with chemometric methods for geographic origin of sesame oils and quantitate analysis of lignans in sesame oils.