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Using the liquid-chromatographic-fingerprint of sterols fraction to discriminate virgin olive from other edible oils A

Bagur-González, M.G., Pérez-Castaño, E., Sánchez-Viñas, M., Gázquez-Evangelista, D.
Journal of chromatography 2015 v.1380 pp. 64-70
Helianthus annuus, canola, chemometrics, cooking fats and oils, corn, liquid chromatography, model validation, models, olives, prediction, principal component analysis, soybeans, sterols, variance, vegetable oil, virgin olive oil
A method to discriminate virgin olive oil from other edible vegetable oils such as, sunflower, pomace olive, rapeseed, canola, corn and soybean, applying chemometric techniques to the liquid chromatographic representative fingerprint of sterols fraction, is proposed. After a pre-treatment of the LC chromatogram data – including baseline correction, smoothing signal and mean centering – different unsupervised and supervised pattern recognition procedures, such as principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least squares-discriminant analysis (PLSDA), have been applied. From the information obtained from PCA and HCA, two groups can be clearly distinguished (virgin olive and the rest of vegetable oils tested) which have been used to discriminate between two defined classes by means of a PLSDA model. Five latent variables (LVs) explained 76.88% of X-block variance and 95.47% of the defined classes block (γ-block) variance. A root mean square error for calibration and cross validation of 0.10 and 0.22 respectively, confirmed these results and a root mean square error for prediction of 0.15 evidences that the classification model proposed presents an adequate prediction capability. The contingency table also shows the good performance of the model, proving the capability of the LC-R-FpM, to discriminate virgin olive from other vegetable edible oils.