Luis Cuadros-Rodríguez

Combining chromatography and chemometrics for the characterization and authentication of fats and oils from triacylglycerol compositional data—A review

The characterization and authentication of fats and oils is a subject of great importance for market and health aspects. Identification and quantification of triacylglycerols in fats and oils can be excellent tools for detecting changes in their composition due to the mixtures of these products. Most of the triacylglycerol species present in either fats or oils could be analyzed and identified by chromatographic methods. However, the natural variability of these samples and the possible presence of adulterants require the application of chemometric pattern recognition methods to facilitate the interpretation of the obtained data. In view of the growing interest in this topic, this paper reviews the literature of the application of exploratory and unsupervised/supervised chemometric methods on chromatographic data, using triacylglycerol composition for the characterization and authentication of several foodstuffs such as olive oil, vegetable oils, animal fats, fish oils, milk and dairy products, cocoa and coffee.

Assessment of uncertainty in pesticide multiresidue analytical methods: main sources and estimation

Abstract The estimation of the uncertainty associated to analytical methods is necessary in order to establish the comparability of results. Multiresidue analytical methods lack very often of information about uncertainty of results with likely implications when results are compared with maximum residue levels (MRL) established by regulations. An adequate identification and estimation of each uncertainty source allows to laboratories to establish the accuracy of results and to balance with time-consuming and costs.

Calibration in chemical measurement processes: I. A metrological approach

A metrological approach to calibration, from the analytical chemistry point of view, is discussed. The terms referring to calibration, and usually employed in the literature (such as calibration point, correction, calibration factor, calibration curve and calibration function) are explained and clarified. Various definitions of calibration, compiled from several international standards, are placed in a metrological context. Likewise, the different types of calibration, which are not always well established, such as direct and indirect calibration, equipment and process calibration are described. Recovery factors and uncertainty in the calibration are also considered.

Olive oil quantification of edible vegetable oil blends using triacylglycerols chromatographic fingerprints and chemometric tools.

The present work studies the effectiveness of the use of triacylglycerols (TAGs) for the quantification of olive oil in blends with vegetable oils. The determinations were obtained using high-performance liquid chromatography (HPLC) coupled to a Charged Aerosol Detector (CAD), in combination with Partial Least Squares (PLS) regression and using interval PLS (iPLS) for variable selection. Results revealed that PLS models can predict olive oil concentrations with reasonable errors. Variable selection through iPLS did not improve predictions significantly, but revealed the chemical information important in the chromatogram to quantify olive oil in vegetable oil blends.

Correction function on biased results due to matrix effects Application to the routine analysis of pesticide residues

A new strategy to carry out the correction of analytical results affected by systematic errors due to the matrix effect is proposed. Two types of external calibrations must be established with the purpose to estimate the matrix effect: solvent calibration (SC) and matrix-matched calibration (MC). These calibration curves are statistically compared and a correction function (CF) is proposed with the aim to simplify the resolution to the problems associated with the incidence of matrix systematic error in the analytical results. Applying this correction function to the results obtained from the solvent calibration, it is possible to make a prediction of the values that would be obtained when the matrix-matched calibration is applied. On the other hand, a rigorous study of the associated uncertainty is developed and applied to the calculated correction function. Finally, this correction function is validated by means of obtained data of recovery studies carried out by a traditional methodology. The methodology has been satisfactorily applied to the quantification of the pesticide procymidone by HPLC for assessing dermal exposure.

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 R(2) 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.

Matrix-effects of vegetable commodities in electron-capture detection applied to pesticide multiresidue analysis

The influence of the sample matrix in the analysis of pesticides in vegetable samples has been studied in order to determine if the matrix content introduces a systematic or proportional (or both) bias in the measurements. Experiments have been carried out during a 4-month period, in which calibration curves, prepared in solvent and in vegetable matrix, were prepared and analysed. A statistical treatment has been applied in order to: (i) check the stability of such calibrations during the period studied; (ii) compare both solvent and matrix-matched calibrations; and (iii) obtain a correction function. Applying the correction function to the results obtained with a solvent calibration it is possible to make a prediction of the values obtained applying a matrix-matched calibration. The performance of the correction function has been validated with recovery data. Finally the uncertainty derived from the use of each calibration plot and the correction function has been calculated.

Discriminating olive and non-olive oils using HPLC-CAD and chemometrics.

AbstractThis 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. FigurePCA scores plot of oil samples. Olive oils (asterisk), non-olive oils (filled square) and oil blends (filled inverse triangle)

Multivariate analysis of HT/GC-(IT)MS chromatographic profiles of triacylglycerol for classification of olive oil varieties

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.

Authentication of geographical origin of palm oil by chromatographic fingerprinting of triacylglycerols and 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%.