Innovative and thorough practice to certify reference materials for sensory defects of olive oil.

An important problem in the olive sector is the occasional mismatch of results obtained by different tasting panels when the same olive oil sample is analysed. These discrepancies could be minimised by using reference materials (RM) for taster training. A comprehensive protocol based on the combined use of sensory and instrumental analysis for the certification of olive oil batches as RMs, developed within the framework of the project 'Operational Group INTERPANEL', is proposed. Similarity indices (R2, cosθ and NEAR) applied on GC-MS fingerprints, allow a successful homogeneity and stability assessment of produced batches. Furthermore, the use of robust statistics combined with a set of instructions developed to remove outliers were applied with excellent results on sensory data set provided by supra-panel composed by more than 100 qualified tasters. This work is the first to provide a comprehensive protocol for certification of real olive oil samples as RM for sensory analysis.

Standardization of chromatographic signals - Part I: Towards obtaining instrument-agnostic fingerprints in gas chromatography.

One of the main causes for the sparse use of multivariate analytical methods in routine laboratory work is the dependency on the measuring instrument from which the analytical signal is acquired. This issue is especially critical in chromatographic equipment and results in limitations of their applicability. The solution to this problem is to obtain a standardized instrument-independent signal -or instrument-agnostic signal- regardless of the measuring instrument or of the state of the same instrument from which it has been acquired. The combined use of both internal and external standard series, allows us to have external and transferable references for the normalization of both the intensity and the position of each element of the data vector being arranged from the raw signal. From this information, a simple mathematical data treatment process is applied and instrument-agnostic signals can be secured. This paper describes and applies the proposed methodology to be followed for obtaining standardized instrumental fingerprints from two significant fractions of virgin olive oil (volatile organic compounds and triacylglycerols), obtained by gas chromatography coupled to mass spectrometry (GC-MS) and analysed with two temperature conditions (conventional and high-temperature, respectively). The results of both case studies show how the instrument-agnostic fingerprints obtained are coincidental, regardless of the state of the chromatographic system or the time of acquisition.

Homogeneity assessment of reference materials for sensory analysis of liquid foodstuffs. The virgin olive oil as case study.

Sensory properties are critical characteristics that determine quality and can be evaluated by trained tasting panels. The panels function as multi-sensor measuring instrument and need the use of reference materials (RMs) for training. The homogeneity between units packaged from a batch of RM can be evaluated by gas chromatography coupled to flame ionization detection (GC-FID), using this instrumental technique as an alternative to sensory analysis. For this purpose, the fingerprint methodology is applied, taking into account that the homogeneity assessment will be based on evaluating the similarity between the fingerprints of the fraction of volatile organic compounds acquired from samples representative of the batch. The proposed methodology is applied with good results to evaluate the homogeneity of several RMs for sensory analysis of virgin olive oil samples, using similarity indices, control charts and exploratory analysis of multivariate data to observe the grouping RM and fingerprint regions representative of each defect.

Intact charge variant analysis of ziv-aflibercept by cationic exchange liquid chromatography as a proof of concept: Comparison between volatile and non-volatile salts in the mobile phase.

Ziv-aflibercept (ziv-AFL) is a complex fusion protein which is widely used in hospitals for the treatment of colorectal metastatic cancer. Charge variants are critical attributes for assessing post-transitional modifications (PMTs) that have to be controlled during the development and manufacture of these proteins and until their administration to patients. Cation exchange (CEX) chromatography is a charge-sensitive analytical method that is well suited for analysing charge variants in proteins. The aim of this paper is to analyse the charge variants of ziv-AFL in the medicine (Zaltrap®) when fresh and when degraded. Two CEX chromatographic methods were compared for this purpose. The former was an adaptation of the method used in the first published study in which charge variants were analysed via pH gradient elution using volatile, low ionic strength buffers with direct coupling to high-resolution Orbitrap mass spectrometry. The second method was developed and optimized during our research using the salt-mediated pH gradient mode and classical non-volatile, high ionic strength buffers which were incompatible with direct coupling with mass detection. Fresh and controlled degraded samples of ziv-AFL were used to evaluate the capacity of both CEX chromatographic strategies for detecting charge variants in ziv-AFL. In the controlled degradation study the samples of the medicine were subjected to three stress factors: temperature of 60 °C for three hours, freeze/thaw process -two cycles-, and exposure to light for twelve hours. The CEX chromatographic method with non-volatile salts in the mobile phase enabled better detection of charge variants degraded ziv-AFL samples than the method using volatile salts with lower ionic strength. In addition, the complexity of the mass spectra data generated made it impossible to identify the multicharge variant species of ziv-AFL. Although charge variants were not separated in ziv-AFL fresh sample, our results indicate that the method with non-volatile salts in the mobile phase could be used to characterize and track changes in the charge variant UV chromatographic profile of ziv-AFL in fresh and degraded samples, even though it cannot be coupled to a mass detector and there is therefore no information about mass. The increase of basic protein degraded compounds were the most important degradation pattern detected in ziv-AFL (Zaltrap®).

The occurrence: A meaningful parameter to be considered in the validation of multivariate classification-based screening methods - Application for authenticating virgin olive oil.

The development of multivariate screening analytical methods in the analytical chemistry field focused particularly on food authentication is growing in recent years, which is evidenced by the increase of scientific publications. Currently there are several guides and technical reports about how -univariate qualitative methods should be properly validated to produce reliable and accurate (fitted-for-purpose) results. Nevertheless, this is not the case when multivariate methods are considered. Aimed at redressing this untenable disadvantage, this paper proposes some guidelines for the validation of multivariate classification-based screening methods. As an application example, the detection of adulteration of virgin olive oil with any other edible vegetal oils is showed. The analytical techniques employed are liquid chromatography coupled to diode array detector (LC-DAD) and gas chromatography coupled to flame ionization detector (GC-FID). For the correct validation of the multivariate screening method a new parameter which never considered before, named occurrence, is accounted. Also, it has been developed two new applicability indicators of the multivariate screening methods: the assignation error index (IERROR) and the index saving (ISAVING) to establish the validation requirements. Then the validation parameters of the methods: precision (or target predictive value), sensitivity, non-target predictive value, specificity and accuracy were estimated. The main conclusion of the work has been the need to take accounts the occurrence value to establish the specific validation requirements to apply the multivariate screening method in a particular scenario.

Authentication of the geographical origin of extra-virgin olive oil of the Arbequina cultivar by chromatographic fingerprinting and chemometrics.

This paper proposes to use chromatographic fingerprints coupled to multivariate techniques to authenticate the geographical origin of extra-virgin olive oils (EVOO) of the Arbequina botanical variety. This methodology uses the whole or part of the chromatogram as input data for the classification models but does not identify or quantify the chemicals constituents. Arbequina monovarietal EVOOs from three geographical origins were studied: two from adjacent European Protected Designation of Origin areas, Siurana and Les Garrigues, in Catalonia in the northeast of Spain; and the third from the south of Spain (Andalucia and Murcia). Three chromatographic fingerprints of each sample were obtained by both reverse and normal phase liquid chromatography coupled to charged aerosol detector (HPLC-CAD), and high temperature gas chromatography coupled to flame ionization detector [(HT)GC-FID]. Principal component analysis (PCA) was used as exploratory technique and soft independent modelling of class analogy (SIMCA) and partial least square-discriminant analysis (PLS-DA) were used as classification methods. High and low-level data fusion strategies were also applied to improve the classification results obtained when the data acquired from each analytical technique were separately used. The results were best for the PLS-DA model with low-level fusion of two techniques (HT)GC-FID with HPLC-CAD, independently of the phase mode. Sensitivity and specificity were 100% in almost all classes, error was 0% for all classes and an inconclusive ratio of just 4% was obtained for the Les Garrigues class due to double assignations.

Comparative Stability Studies of Different Infliximab and Biosimilar CT-P13 Clinical Solutions by Combined Use of Physicochemical Analytical Techniques and Enzyme-Linked Immunosorbent Assay (ELISA).

BACKGROUND: There are two products in which infliximab is the active pharmaceutical ingredient. These are Remicade® (INF; reference product) and Remsima™/Inflectra™ (CT-P13; infliximab biosimilar). Remsima™/Inflectra™ are bioidentical products. Different recommendations have been made for the clinical solutions of each brand (Remicade® or Remsima™/Inflectra™) despite the manufacturer of the biosimilar claiming high levels of similarity to the innovator. OBJECTIVE: The objective of this study was to assess and compare stability against degradation and over time of different clinical infliximab solutions prepared from Remicade® and from Remsima™/Inflectra™ using a suitable set of characterization methods in line with the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) recommendations. METHODS: Reconstituted solutions of INF and CT-P13 and dilutions as used in hospital were stored in glass vials (10 and 2 mg/mL) or in polyolefin infusion bags (0.4 mg/mL) refrigerated between 2 and 8 °C for 2 weeks. Regarding the physicochemical properties, the distribution of the particulates were studied over a range of 0.001-1 µm by dynamic light scattering (DLS) and oligomers up to 8 monomer were analyzed by native size-exclusion ultra-high-performance liquid chromatography with ultraviolet (UV)-visible detection coupled to (native) mass spectrometry (SE/UHPLC-UV-(native) MS); mass spectrometry was also used to evaluate natural aggregates and isoform profile; DLS was also employed to detect gross conformational changes by tracking the hydrodynamic radius (HR). The secondary structure of the proteins was studied by far UV circular dichroism (CD). The tertiary structure was investigated by intrinsic tryptophan fluorescence (IT-F). Reverse-phase ultra-high-performance liquid chromatography with UV detection (RP/UHPLC-UV) was used to analyze intact INF and CT-P13 for quantification purposes. Functionality was evaluated via the biological activity measured by the extension of the immunological reaction of the INF and the CT-P13 with its antigen, i.e., the tumor necrosis factor-α by enzyme-linked immunosorbent assay (ELISA). RESULTS: The stress applied to INF and CT-P13 solutions showed similar levels of aggregate formation, structural variation, and chemical modifications. The only noteworthy difference between INF and CT-P13 was detected in their behavior to freeze-thaw cycles, in which CT-P13 showed slightly more robustness. INF and CT-P13 showed identical CD spectra, similar to those reported for IgG1 in which there is dominance in ß sheet secondary structures; this typical conformation remained unmodified over time in INF and CT-P13. No significant changes were detected in the tertiary structure and no aggregates process was noticed over the time studied. Polydispersity slightly increased for the most concentrated solutions, while there were no meaningful differences in the HR in the solutions over time. The concentration of INF and CT-P13 also remained constant. Differences in the native isoform MS profile were detected, as expected by the different glycosylation pattern, with no important modification over time. Functionality was maintained over the test period (60 days) and was similar in all the solutions tested, with no differences between INF and biosimilar solutions. CONCLUSIONS: High levels of similarity were noticed in the behavior of INF and CT-P13 when subjected to stress. When stored refrigerated at between 2 and 8 °C and prepared as normally used in the hospital pharmacy, all solutions showed physicochemical and functional stability for all the concentrations tested and all containers, at least for the 14-day test period.

Development and validation of a (RP)UHPLC-UV-(HESI/Orbitrap)MS method for the identification and quantification of mixtures of intact therapeutical monoclonal antibodies using a monolithic column.

Monoclonal antibodies (mAbs) are one of the most important types of biopharmaceutics and have proved enormously successful in the treatment of cancers and autoimmune diseases. In this paper, we present a fast, straightforward reversed phase (RP)UHPLC-UV-(HESI/Orbitrap) MS method for the separation and identification of five of the most commonly used mAbs, i.e. bevazizumab (BEV), cetuximab (CTX), infliximab (INF), rituximab (RTX) and trastuzumab (TTZ) in mixtures. The RP mAbs separation was performed in a divinylbenzene-based monolithic column, after statistical design of the experiments with a novel approach for optimizing chromatographic conditions called the heteroscedasticity function. Results led us to split the initial mixture of five mAbs into two mixtures with four mAbs each, one containing RTX and the other TTZ. The method was validated for quantification using the signal from the UV detector and identification by (HESI-Orbitrap)MS. Direct MS characterization of the intact isoform profile of each mAb was also obtained. Advantages and disadvantages of the use of trifluoroacetic acid or formic acid as ion pairing agents for mass spectrometric analysis and chromatographic separation are discussed. Validation was performed using an internal protocol based on well-known international guidelines such as the International Conference on Harmonization (ICH) guideline, the US Food and Drugs Administration (FDA) guideline and the United Stated Pharmacopeia (USP) guideline. Performance parameters such as linearity, accuracy (precision and trueness), detection limits, quantification limits and robustness were evaluated. Robustness was established by studying the total and one-sided effects of four selected variables: column temperature, trifluoroacetic acid content in the mobile phases, initial proportion of eluent B and gradient. The results indicated the suitability of this method for quantifying these five mAbs in mixtures, as well as its robustness, reproducibility and sensitivity.

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis - part I: framework and fundamentals.

BACKGROUND: Virgin olive oil is the only food product for which sensory analysis is regulated to classify it in different quality categories. To harmonize the results of the sensorial method, the use of standards or reference materials is crucial. The stability of sensory reference materials is required to enable their suitable control, aiming to confirm that their specific target values are maintained on an ongoing basis. Currently, such stability is monitored by means of sensory analysis and the sensory panels are in the paradoxical situation of controlling the standards that are devoted to controlling the panels. RESULTS: In the present study, several approaches based on similarity analysis are exploited. For each approach, the specific methodology to build a proper multivariate control chart to monitor the stability of the sensory properties is explained and discussed. CONCLUSION: The normalized Euclidean and Mahalanobis distances, the so-called nearness and hardiness indices respectively, have been defined as new similarity indices to range the values from 0 to 1. Also, the squared mean from Hotelling's T2 -statistic and Q2 -statistic has been proposed as another similarity index. © 2018 Society of Chemical Industry.

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis - part II: applications.

BACKGROUND: The organoleptic quality of virgin olive oil depends on positive and negative sensory attributes. These attributes are related to volatile organic compounds and phenolic compounds that represent the aroma and taste (flavour) of the virgin olive oil. The flavour is the characteristic that can be measured by a taster panel. However, as for any analytical measuring device, the tasters, individually, and the panel, as a whole, should be harmonized and validated and proper olive oil standards are needed. RESULTS: In the present study, multivariate approaches are put into practice in addition to the rules to build a multivariate control chart from chromatographic volatile fingerprinting and chemometrics. Fingerprinting techniques provide analytical information without identify and quantify the analytes. This methodology is used to monitor the stability of sensory reference materials. CONCLUSION: The similarity indices have been calculated to build multivariate control chart with two olive oils certified reference materials that have been used as examples to monitor their stabilities. This methodology with chromatographic data could be applied in parallel with the 'panel test' sensory method to reduce the work of sensory analysis. © 2018 Society of Chemical Industry.

HPLC-UV and HPLC-CAD chromatographic data fusion for the authentication of the geographical origin of palm oil.

Data fusion combined with a multivariate classification approach (partial least squares-discriminant analysis, PLS-DA) was applied to authenticate the geographical origin of palm oil. Data fusion takes advantage of the synergistic effect of information collected from more than one data source. In this study, data from liquid chromatography coupled to two detectors -ultraviolet (UV) and charged aerosol (CAD)- was fused by high- and mid-level data fusion strategies. Mid-level data fusion combines a few variables from each technique and then applies the classification technique. Principal component analysis and interval partial least squares were applied to obtain the variables selected. High-level data fusion combines the PLS-DA classification results obtained individually from the chromatographic technique with each detector. Fuzzy aggregation connective operators were used to make the combinations. Prediction rates varied between 73% and 98% for the individual techniques and between 87% and 100% and 93% and 100% for the mid- and high-level data fusion strategies, respectively.

A new analytical method for quantification of olive and palm oil in blends with other vegetable edible oils based on the chromatographic fingerprints from the methyl-transesterified fraction.

A new analytical method for the quantification of olive oil and palm oil in blends with other vegetable edible oils (canola, safflower, corn, peanut, seeds, grapeseed, linseed, sesame and soybean) using normal phase liquid chromatography, and applying chemometric tools was developed. The procedure for obtaining of chromatographic fingerprint from the methyl-transesterified fraction from each blend is described. The multivariate quantification methods used were Partial Least Square-Regression (PLS-R) and Support Vector Regression (SVR). The quantification results were evaluated by several parameters as the Root Mean Square Error of Validation (RMSEV), Mean Absolute Error of Validation (MAEV) and Median Absolute Error of Validation (MdAEV). It has to be highlighted that the new proposed analytical method, the chromatographic analysis takes only eight minutes and the results obtained showed the potential of this method and allowed quantification of mixtures of olive oil and palm oil with other vegetable oils.

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods.

A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phase LC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis took only 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil were used: one input-class, two input-class, and pseudo two input-class.

One input-class and two input-class classifications for differentiating olive oil from other edible vegetable oils by use of the normal-phase liquid chromatography fingerprint of the methyl-transesterified fraction.

A new method for differentiation of olive oil (independently of the quality category) from other vegetable oils (canola, safflower, corn, peanut, seeds, grapeseed, palm, linseed, sesame and soybean) has been developed. The analytical procedure for chromatographic fingerprinting of the methyl-transesterified fraction of each vegetable oil, using normal-phase liquid chromatography, is described and the chemometric strategies applied and discussed. Some chemometric methods, such as k-nearest neighbours (kNN), partial least squared-discriminant analysis (PLS-DA), support vector machine classification analysis (SVM-C), and soft independent modelling of class analogies (SIMCA), were applied to build classification models. Performance of the classification was evaluated and ranked using several classification quality metrics. The discriminant analysis, based on the use of one input-class, (plus a dummy class) was applied for the first time in this study.

Combined untargeted and targeted fingerprinting with comprehensive two-dimensional chromatography for volatiles and ripening indicators in olive oil.

Comprehensive two-dimensional gas chromatography (GC × GC) is the most effective multidimensional separation technique for in-depth investigations of complex samples of volatiles (VOC) in food. However, each analytical run produces dense, multi-dimensional data, so elaboration and interpretation of chemical information is challenging. This study exploits recent advances of GC × GC-MS chromatographic fingerprinting to study VOCs distributions from Extra Virgin Olive Oil (EVOO) samples of a single botanical origin (Picual), cultivated in well-defined plots in Granada (Spain), and harvested at different maturation stages. A new integrated work-flow, fully supported by dedicated and automated software tools, combines untargeted and targeted (UT) approaches based on peak-region features to achieve the most inclusive fingerprinting. Combined results from untargeted and targeted methods are consistent, reliable, and informative on discriminant features (analytes) correlated with optimal ripening of olive fruits and sensory quality of EVOOs. The great flexibility of the UT fingerprinting here adopted enables retrospective analysis with great confidence and provides data to validate the transferability of ripening indicators ((Z)-3-hexenal, (Z)-2-hexenal, (E)-2-pentenal, nonanal, 6-methyl-5-hepten-2-one, octane) to external samples sets. Direct image comparison, based on visual features, also is investigated for quick and effective pair-wise investigations. Its implementation with reliable metadata generated by UT fingerprinting confirms the maturity of 2D data elaboration tools and makes advanced image processing a real perspective.

Study and ICH validation of a reverse-phase liquid chromatographic method for the quantification of the intact monoclonal antibody cetuximab.

Cetuximab (CTX) is a potent chimeric mouse/human monoclonal antibody (mAb) approved worldwide for treatment of metastatic colorectal cancer. Among the various biological and physical analyses performed for full study on this biopharmaceutic, the determination of the concentration preparations throughout manufacturing and subsequent handling in hospital is particularly relevant. In the present work, the study and validation of a method for quantifying intact CTX by reverse-phase high-performance liquid chromatography with diode array detection ((RP)HPLC/DAD) is presented. With that end, we checked the performance of a chromatographic method for quantifying CTX and conducted a study to validate the method as stability-indicating in accordance with the International Conference on Harmonization guidelines (ICH) for biotechnological drugs; therefore, we evaluated linearity, accuracy, precision, detection and quantification limits, robustness and system suitability. The specificity of the method and the robustness of the mAb formulation against external stress factors were estimated by comprehensive chromatographic analysis by subjecting CTX to several informative stress conditions. As demonstrated, the method is rapid, accurate, and reproducible for CTX quantification. It was also successfully used to quantify CTX in a long-term stability study performed under hospital conditions.

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

The 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.

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%.

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.