Combination of fingerprint and chemometric analytical approaches to identify the geographical origin of Qinghai-Tibet plateau rapeseed oil

Abstract

Verification of the geographical origin of rapeseed oil is essential to protect consumers from fraudulent products. A prospective study was conducted on 45 samples from three rapeseed oil-producing areas in Qinghai Province, which were analyzed by GC-FID and GC-MS. To assess the accuracy of the prediction of origin, classification models were developed using PCA, OPLS-DA, and LDA. It was found that multivariate analysis combined with PCA separate 96% of the samples, and the correct sample discrimination rate based on the OPLS-DA model was over 98%. The predictive index of the model was Q2 = 0.841, indicating that the model had good predictive ability. The LDA results showed highly accurate classification (100%) and cross-validation (100%) rates for the rapeseed oil samples, demonstrating that the model had strong predictive capacity. These findings will serve as a foundation for the implementation and advancement of origin traceability using the combination of fatty acid, phytosterol and tocopherol fingerprints.