Camellia oil adulteration detection using fatty acid ratios and tocopherol compositions with chemometrics

The fatty acid ratios and tocopherol compositions, coupled to chemometrics were used to verify camellia oil (CAO) adulteration with corn oil (COO), rapeseed oil (RAO), rice bran oil (RBO), sesame oil (SEO), and soybean oil (SOO). Using hierarchical clustering analysis (HCA), all CAO samples were classified into one category accounting for their high oleic acid/α-linolenic acid, oleic acid/palmitic acid, oleic acid/linoleic acid ratios, and α-tocopherol%. Based on partial least squares-discriminant analysis (PLS-DA), well clustering could be observed among CAO, CAO + COO, CAO + SOO, CAO + RBO, CAO + SEO, as well as CAO + RAO samples, with higher than 97.67% of the total discrimination accuracy, at an adulteration ratio above 40%. Using soft-independent modelling of class analogy (SIMCA), the adulterated CAO samples (5%–100%) possessed good correct classification rates above 90.00%. Both two complementary classification methods were confirmed to be feasible and accurate for camellia oil authentication.