Identification and quantitation of multiplex camellia oil adulteration based on 11 characteristic lipids using UPLC-Q-Orbitrap-MS

Currently, the identification and quantification of complex adulteration of high-value vegetable oils are still challenging. In this study, the extreme vertex design method was adopted to design representative multivariate adulterated camellia oil samples. Thereafter, 11 characteristic lipid species were identified by considering the statistically significant difference and categorical contribution. A discriminant method and linear regression model were established based on 11 key lipids. The accuracy rate of the model was 100 %, which could correctly discriminate the camellia oil with an adulteration ratio as low as 2.5 %. The root mean square error (RMSE) of the regression equation was close to 0, and the coefficient of determination (R2P) was 0.9054. This method has been successfully applied to commercially available samples for validation purposes, detecting 27.3 % of camellia oil adulteration. Overall, the results indicate that the discriminating method and content prediction model can provide a potential strategy for authentication of multivariate vegetable oils. [Display omitted] •Extreme vertex design was used to create multivariate adulterated camellia oils.•Eleven lipid species with significant variations were found using lipidomics.•A reliable adulteration discriminant method was established based on characteristic lipids.•Peak areas of characteristic lipids were modelled to estimate camellia oil concentration.