Quantification of blending of olive oils and edible vegetable oils by triacylglycerol fingerprint gas chromatography and chemometric tools

A reliable procedure for the identification and quantification of the adulteration of olive oils in terms of blending with other vegetable oils (sunflower, corn, seeds, sesame and soya) has been developed. From the analytical viewpoint, the whole procedure relies only on the results of the determination of the triacylglycerol profile of the oils by high temperature gas chromatography–mass spectrometry. The chromatographic profiles were pre-treated (baseline correction, peak alignment using iCoshift algorithm and mean centering) before building the models. At first, a class-modeling approach, Soft Independent Modeling of Class Analogy (SIMCA) was used to identify the vegetable oil used blending. Successively, a separate calibration model for each kind of blending was built using Partial Least Square (PLS). The correlation coefficients of actual versus predicted concentrations resulting from multivariate calibration models were between 0.95 and 0.99. In addition, Genetic algorithms (GA–PLS), were used, as variable selection method, to improve the models which yielded R2 values higher than 0.90 for calibration set. This model had a better predictive ability than the PLS without feature selection. The results obtained showed the potential of this method and allowed quantification of blends of olive oil in the vegetable oils tested containing at least 10% of olive oil

Geographical provenance of palm oil by fatty acid and volatile compound fingerprinting techniques

Analytical methods are required in addition to administrative controls to verify the geographical origin of vegetable oils such as palm oil in an objective manner. In this study the application of fatty acid and volatile organic compound fingerprinting in combination with chemometrics have been applied to verify the geographical origin of crude palm oil (continental scale). For this purpose 94 crude palm oil samples were collected from South East Asia (55), South America (11) and Africa (28). Partial least squares discriminant analysis (PLS-DA) was used to develop a hierarchical classification model by combining two consecutive binary PLS-DA models. First, a PLS-DA model was built to distinguish South East Asian from non-South East Asian palm oil samples. Then a second model was developed, only for the non-Asian samples, to discriminate African from South American crude palm oil. Models were externally validated by using them to predict the identity of new authentic samples. The fatty acid fingerprinting model revealed three misclassified samples. The volatile compound fingerprinting models showed an 88%, 100% and 100% accuracy for the South East Asian, African and American class, respectively. The verification of the geographical origin of crude palm oil is feasible by fatty acid and volatile compound fingerprinting. Further research is required to further validate the approach and to increase its spatial specificity to country/province scale

Authentication of geographical origin of palm oil by chromatographic fingerprinting of triacylglycerols an partial least square-discriminant analysis

Main goals of the present work were to develop authentication models based on liquid and gas chromatographic fingerprinting of triacylglycerols (TAGs) from palm oil of different geographical origins in order to compare them. For this purpose, a set of palm oil samples were collected from different continents: South eastern Asia, Africa and South America. For the analysis of the information in these fingerprint profiles, a pattern recognition technique such as partial least square discriminant analysis (PLS-DA) was applied to discriminate the geographical origin of these oils, at continent level. The liquid chromatography, coupled to a charged aerosol detector, (HPLC–CAD) TAGs separation was optimized in terms of mobile phase composition and by means of a solid silica core column. The gas chromatographic method with a mass spectrometer was applied under high temperature (HTGC–MS) in order to analyze the intact TAGs. Satisfactory chromatographic resolution within a short total analysis time was achieved with both chromatographic approaches and without any prior sample treatment. The rates of successful in prediction of the geographical origin of the 85 samples varied between 70% and 100%

Proton transfer reaction-mass spectrometry volatile organic compound fingerprinting for monovarietal extra virgin olive oil identification

Proton transfer reaction-mass spectrometry (PTR-MS) is a relatively new technique that allows the fast and accurate qualification of the volatile organic compound (VOC) fingerprint. This paper describes the analysis of thirty samples of extra virgin olive oil, of five different varieties of olive fruit (Arbequina, Cornicabra, Frantoio, Hojiblanca, and Picual) by PTR-MS. A multivariate pattern recognition method (partial least square-discriminant analysis, PLS-DA) was applied on the full spectra fingerprint of the PTR-MS measurements. The multivariate model was doubly validated: firstly by means of internal validation (cross-validation) and secondly with an external validation data set. The results showed that the five varieties could be successfully distinguished within them. The proposed method provides a new valuable tool for extra virgin olive oil classification according to variety, and it could serve as a screening technique for the authentication of monovarietal extra-virgin olive oil and as a methodology to confirm that a variety is in agreement with claimed identity