Combining GC-MS and chemometrics to assess the quality of camellia seed oils

Abstract

Chemometric tools and GC-MS were employed to detect the adulteration of camellia seed oil. In the training set, 65 samples were formulated by blending pure camellia seed oil with varying concentrations of soybean, peanut and rapeseed oil and their fatty acid contents were used in chemometric analysis. Principal Component analysis revealed the distribution patterns of samples that showed a clear stratiform agglomeration according to varying amount of camellia seed oil. Fisher Discriminant Analysis (FDA) was combined to establish the classification models and the correct classification rate of cross-validation model and original model reached 92.5% and 82.5%, respectively. To test the quality of the model, a new test set was prepared and the results showed a high accuracy of 88%. This was indicated that the developed discrimination model established by PCA plus FDA was efficient in predicting the presence of other low-price edible oils in camellia seed oils.