Cristina Ruiz-Samblás

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.

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.

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%.

Application of selected ion monitoring to the analysis of triacylglycerols in olive oil by high temperature-gas chromatography/mass spectrometry.

The analysis of the triacylglycerol (TAG) composition of oils is a very challenging task, since the TAGs have very similar physico-chemical properties. In this work, a high temperature-gas chromatographic method coupled to electron ionization-mass spectrometry (HT-GC/EI-MS), in the Selected Ion Monitoring (SIM) mode, method was developed for the analysis of TAGs in the olive oil; this is a method suitable for routine analysis. This method was developed using commercially available standard TAGs. The TAGs studied were separated according to their equivalent carbon number and degree of unsaturation. The peak assignment was carried out by locating the characteristic fragment ions having the same retention time on the SIM profile such as [RCO+74](+) and [RCO+128](+) ions, due to the fatty acyl residues on sn-1, sn-2 and sn-3 positions of the TAG molecule and the [M-OCOR](+) ions corresponding to the acyl ions. The developed method was very useful to eliminate the interferences that appeared in the mass spectrum since electron ionization can prevent satisfactory interpretation of spectra.

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.

Comparison of different analytical classification scenarios: application for the geographical origin of edible palm oil by sterolic (NP) HPLC fingerprinting

This work shows how the best scenario, which applies two chemometric classifiers on different analytical datasets from the same sample set, could be chosen according to the classification results. To this end, several classification quality features such as sensitivity (or recall), specificity, positive (or precision) and negative predictive values, Youden index, positive and negative likelihood ratios, F-measure (or F-score), discriminant power, efficiency (or accuracy), AUC (area under the receiver operating curve), Matthews correlation coefficient, Kappa coefficient, overall agreement probability, overall agreement probability from chance and overall Kappa coefficient are described and discussed. As an application example, two sterolic chromatographic fingerprints obtained from two different normal-phase HPLC systems are used to discern the geographical origin (South-East Asia, West Africa and South America) of edible palm oil. In each case, two conventional and well-known chemometric classification methods are applied: soft independent modelling by class analogy (SIMCA) and partial least squares-discriminant analysis (PLS-DA).

Triacylglycerols Determination by High-temperature Gas Chromatography in the Analysis of Vegetable Oils and Foods: A Review of the Past 10 Years

The analysis of triacylglycerols by high-temperature gas chromatography, along the last 10 years has been reviewed in this paper. The interest in this topic has grown along the last years due to the triacylglycerols are the main components of oils and fats and they are being used for the characterization and authentication of foods products. The most commonly used procedures, including the official methodologies, applying high-temperature gas chromatographic techniques are shown. Their importance in the characterization of different kind of samples, vegetable oils, seeds, dairy products, etc., is considered. This review is not intended to be a comprehensive dissertation on the field of triacylglycerols analysis since that would require sufficient space to occupy a book in its own right. Rather, it will outline selected considerations and developments, where the technique has been applied.

Application of data mining methods for classification and prediction of olive oil blends with other vegetable oils

AbstractThe aim of this article is to study tree-based ensemble methods, new emerging modelling techniques, for authentication of samples of olive oil blends to check their suitability for classifying the samples according to the type of oil used for the blend as well as for predicting the amount of olive oil in the blend. The performance of these methods has been investigated in chromatographic fingerprint data of olive oil blends with other vegetable oils without needing either to identify or to quantify the chromatographic peaks. Different data mining methods—classification and regression trees, random forest and M5 rules—were tested for classification and prediction. In addition, these classification and regression tree approaches were also used for feature selection prior to modelling in order to reduce the number of attributes in the chromatogram. The good outcomes have shown that these methods allow one to obtain interpretable models with much more information than the traditional chemometric methods and provide valuable information for detecting which vegetable oil is mixed with olive oil and the percentage of oil used, with a single chromatogram. Figureᅟ

Exploratory data analysis in the study of 7Be present in atmospheric aerosols.

PurposeExploratory data analysis (EDA) is applied in this research to study the behavior of radioactive aerosols present in the surface atmosphere of Granada, using 7Be as radiotracer. The reason for this study is to reduce the large number of parameters involved in understanding their behavior, given the complexity of the atmosphere.MethodsAerosol particles were collected weekly in Granada (Spain) over a 5-year period. Low-background gamma spectrometry was used to determine concentrations of 7Be-aerosol activity. The variables studied were: 7Be concentration, cosmic ray intensity, temperature, temperature interval, rainfall, relative humidity, and Saharan intrusions. Least significant difference test (LSD), hierarchical cluster analysis (HCA), and principal component analysis (PCA) with varimax rotation have been applied to study the datasets.Results and discussionThe results of our study reveal that aerosol behavior is represented by two principal components which explain 86.23 % of total variance. Components PC1 and PC2 respectively explain 74.61 and 11.62 % of total variance. PC1 explains the cyclical and seasonal pattern of the samples, while PC2 is related to the production of 7Be. In addition, PCA and HCA show good distribution of the samples by families with two groups, summer and winter, at the extremes and spring–autumn in the middle. This result corroborates that there are no differences between spring and autumn in the climate of Granada.ConclusionsEDA has been found to be quite useful in studying the behavior of radioactive aerosols in the surface atmosphere of a city with the climate and geographical characteristics of Granada.