Juan Manuel Bosque-Sendra

Dispersive liquid–liquid microextraction (DLLME) has been proposed for the extraction and preconcentration of 12 carbamate pesticides in juice samples, followed by their determination by micellar electrokinetic chromatography with diode-array detection. To improve sensitivity, an on-capillary sample concentration method based on sweeping has been developed. Also, separations were performed in an extended light path fused-silica capillary; the separation buffer consisted of 100 mM borate and 50 mM SDS (pH 9.0) with 5% acetonitrile. Samples were introduced by hydrodynamic injection, dissolved in the separation buffer, but free of micelles. Several parameters of the DLLME procedure (such as type and volume of extraction and dispersive solvents, pH, salt addition, and extraction time) were optimized. Recoveries obtained for fortified juice samples (banana, pineapple, and tomato) at three different concentration levels, ranged from 78% to 105%, with relative standard deviations lower than 9%. The limits of detection ranged from 1 to 7 μg l⁻¹. Moreover, the method is fast, simple, and environmentally friendly.

The ability of multivariate analysis methods such as hierarchical cluster analysis, principal component analysis and partial least squares-discriminant analysis (PLS-DA) to achieve olive oil classification based on the olive fruit varieties from their triacylglycerols profile, have been investigated. The variations in the raw chromatographic data sets of 56 olive oil samples were studied by high-temperature gas chromatography with (ion trap) mass spectrometry detection. The olive oil samples were of four different categories (“extra-virgin olive oil”, “virgin olive oil”, “olive oil” and “olive-pomace” oil), and for the “extra-virgin” category, six different well-identified olive oil varieties (“hojiblanca”, “manzanilla”, “picual”, “cornicabra”, “arbequina” and “frantoio”) and some blends of unidentified varieties. Moreover, by pre-processing methods of chemometric (to linearise the response of the variables) such as peak-shifting, baseline (weighted least squares) and mean centering, it was possible to improve the model and grouping between different varieties of olive oils. By using the first three principal components, it was possible to account for 79.50% of the information on the original data. The fitted PLS-DA model succeeded in classifying the samples. Correct classification rates were assessed by cross-validation.

This work presents a method for an efficient differentiation of olive oil and several types of vegetable oils using chemometric tools. Triacylglycerides (TAGs) profiles of 126 samples of different categories and varieties of olive oils, and types of edible oils, including corn, sunflower, peanut, soybean, rapeseed, canola, seed, sesame, grape seed, and some mixed oils, have been analyzed. High-performance liquid chromatography coupled to a charged aerosol detector was used to characterize TAGs. The complete chromatograms were evaluated by PCA, PLS-DA, and MCR in combination with suitable preprocessing. The chromatographic data show two clusters; one for olive oil samples and another for the non-olive oils. Commercial oil blends are located between the groups, depending on the concentration of olive oil in the sample. As a result, a good classification among olive oils and non-olive oils and a chemical justification of such classification was achieved.