Geographical Characterization of Greek Olive Oils Using Rare Earth Elements Content and Supervised Chemometric Techniques

Different ANNs models [Multi-layer Perceptrons (MLPs) and Radial Basis Function (RBF)] were developed and evaluated for the discrimination of olive oils produced in four Greek regions according to their geographical origin. For this purpose, ninety-seven samples were analyzed for 10 rare earth elements (REE) by ICP-MS. Moreover, two additional supervised techniques, discriminant analysis (DA) and classification trees (CTs), were applied to the same set for the data pre-treatment and for comparison purposes. In addition, two approaches were used for models' training and evaluation: the classical random choice of samples for the learning data set and an innovative one, which used the two linear discriminant functions (LDFs) of the preceding DA to choose the most representative learning sample set. The results were very satisfactory for the new ANNs classifiers. Over-fitting phenomena were overcome and the prediction ability was 73%, as evaluated by an independent test sample set. The results are encouraging for the ANNs efficiency even in demanding data bases, as the one under consideration.

[Supplementary materials are available for this article. Go to the publisher's online edition of Analytical Letters for the following free supplemental resources: Additional figures and tables.]     

Recognition of volatile compounds as markers in geographical discrimination of Spanish extra virgin olive oils by chemometric analysis of non-specific chromatography volatile profiles

Chromatographic profiles obtained by headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography (GC) were processed as continuous and non-specific signals through multivariate analysis techniques in order to select and identify the most discriminate volatile marker compounds related to the geographical origin of extra virgin olive oils. The blind analysis of the chromatographic profiles was carried out on several steps including preliminary mathematical treatments, explorative analysis, feature selection and classification. The results obtained through the application of stepwise linear discriminant analysis (SLDA) method revealed a perfect discrimination between the different Spanish geographical regions considered (La Rioja, Andalusia and Catalonia). The assignment success rate was 100% in both classification and prediction by using cross validation procedure. In addition, it must be noted that the proposed strategy was able to verify the geographical origin of the samples involving only a reduced number of discriminate retention times selected by the stepwise procedure. This fact emphasizes the quality of the accurate results obtained and encourages the feasibility of similar procedures in olive oil quality and traceability studies. Finally, volatile compounds corresponding to the predictors retained were identified by gas chromatography-mass spectrometry (GC-MS) for a chemical interpretation of their importance in quality virgin olive oils.     

Determination of trace element in Italian virgin olive oils and their characterization according to geographical origin by statistical analysis

Multi-element analysis of organic virgin olive oils from different Italian regions was carried out by inductively coupled plasma mass spectrometry (ICP-MS) aiming at developing a reliable method in the traceability of the origin of oils. The data were processed by means of the chemiometric approach of linear discriminant analysis (LDA) that allows classifying unknown samples after checking possible differentiation of samples of known origin.An external calibration curve was build for the quantitative analysis. The calibration curves for each element were linear in the range between 0.01 and 100 ng mL⁻¹ and 0.2 to 2000 ng mL⁻¹, the correlation coefficients were ranging between 0.996 and 0.999. Results from spike and recovery experiments at levels of 30 and 65 ng mL⁻¹ were in the range of 91-119%, whereas the quantitation limits, based on 10 times standard deviation of the blank, were also in the range of 0.009-10.2 ng g⁻¹, for almost all the elements.     

Application of solid-phase microextraction to the analysis of volatile compounds in virgin olive oils

Solid-phase microextraction was used as a technique for headspace sampling of extra virgin olive oil and virgin olive oil samples with different off-flavours. A 100 microm coated polydimethylsiloxane fiber was used to extract volatile aldehydes, the sampling temperature was 45 degrees C and the fiber has been exposed to the headspace for 15 min. Nonanal and 2-decenal were present in all the olive oils with extraction off-flavours but were not in extra virgin olive oil sample.     

Geographical classification of wine and olive oil by means of classification and influence matrix analysis (CAIMAN)

Classification and influence matrix analysis (CAIMAN) is a new classification method, recently proposed and based on the influence matrix (also called leverage matrix). Depending on the purposes of the classification analysis, CAIMAN can be used in three outlines: (1) D-CAIMAN is a discriminant classification method, (2) M-CAIMAN is a class modelling method allowing a sample to be classified, not classified at all, or assigned to more than one class (confused) and (3) A-CAIMAN deals with the asymmetric case, where only a reference class needs to be modelled.

In this work, the geographic classification of samples of wine and olive oil has been carried out by means of CAIMAN and its results compared with discriminant analysis, by focusing great attention on the model predictive capabilities. The geographic characterization has been carried out on three different datasets: extra virgin olive oils produced in a small area, with a “protected denomination of origin” label, wines with different denominations of origin, but produced in enclosed geographical areas, and olive oils belonging to different production areas.

Final results seem to indicate that the application of CAIMAN to the geographical origin identification offers several advantages: first, it shows – on an average basis – good performances; second, it is able to deal in a simple way classification problems related to tipicity, authenticity, and uniqueness characterization, which are of increasing interest in food quality issues.     

Classification of extra virgin olive oils according to the protected designation of origin, olive variety and geographical origin

A headspace-mass spectrometry (HS-MS) coupling designed for the sensory characterization and classification of extra virgin olive oil on the basis of its protected designation of origin, olive variety and geographical origin is reported. The procedure involves the headspace generation and the direct injection of the homogenized gaseous phase into a mass spectrometer through a transfer line. The results obtained were chemometrically treated to achieve the best model capable of discriminating between the different olive oil categories. For this purpose, several procedures for variables selection, data pretreatments and unsupervised techniques were evaluated. In addition, K-nearest neighbor and soft independent modeling of class analogy algorithms were employed to the classification models building. Taking into account the prediction results obtained (ca. 87% of samples correctly classified and a specificity of ca. 97%), it can be concluded than the HS-MS coupling is, with an adequate chemometric treatment, an appropriate technique for routine control.     

Direct thermal extraction and gas chromatographic-mass spectrometric determination of volatile compounds of extra-virgin olive oils

The instrumental performances of a Thermo Desorption-Cooled Injection System coupled with a gas chromatography-mass spectrometer (GC-MS) were improved by a Plackett-Burman experimental design for the direct thermal extraction of volatile compounds from extra-virgin olive oils. The obtained experimental conditions were applied to the analysis of samples from West Liguria (cv. Taggiasca > or = 90%) and Spain (cv. Arbequina), which shared such similar sensorial features that Taste Panel did not distinguish them. Principal component analysis (PCA) was then applied to the experimental data. Three linear combinations of the amounts of the lipoxygenase oxidation products proved to be decisive and sufficient in the differentiation of the two groups of samples.     

Determination of trace elements in olive oil by ICP-AES and ETA-AAS: A pilot study on the geographical characterization

The determination of trace elements in edible oils is important because of both the metabolic role of metals and possibilities for adulteration detection and oil characterization.The most commonly used techniques for the determination of metals in oil samples are inductively coupled plasma atomic emission spectrometry (ICP-AES) and atomic absorption spectrometry (AAS). For this study, a microwave assisted decomposition of the olive oil in closed vessels using a mixture of nitric acid and hydrogen peroxide was applied as sample preparation.The low achievable LODs enable the determination by ICP-AES of even very low concentrations of most elements of interest. The proposed ICP-AES method permits the determination of Ca, Fe, Mg, Na, and Zn in olive oils. Elements present in small amounts (Al, Co, Cu, K, Mn, Ni) were measured by ETA-AAS in the same sample digest. The concentrations of Al, Co, Cu, K, Mn, and Ni were in the range from 0.15 to 1.5 μg/g and differ according to the geographical origin of the oils. For the amounts of Fe, Mg, Na, and Zn in the samples, no significant differences according to the geographical origin of the oils could be observed, the mean concentrations being 15.31, 3.26, 33.10, and 3.39 μg/g, respectively. The Ca content varies in the range of 1.3 to 9.0 μg/g.The dependency of the trace elemental content of olive oils on their geographical origin can be used for their local characterization.     

Characterization of the geographical origin of Italian red wines based on traditional and nuclear magnetic resonance spectrometric determinations

Chemical characterization of Italian red wines from different geographical locations in the Apulia, region of southern Italy, have been performed by means of chromatographic, analyses routine analyses (density, alcohol content, acidity, dry extract and ash content), inductively coupled plasma-atomic emission spectrometric measurements and nuclear magnetic resonance (¹H NMR) spectrometric determinations. Multivariate statistical methods were applied separately to the analytical and NMR data. The results showed that Apulian red wines are divided in three groups according to their geographical origin.     

Supervised pattern recognition applied to the discrimination of the floral origin of six types of Italian honey samples

In this work a supervised chemometric approach to the discrimination of Italian honey samples from different floral origin is presented. The analytical data of 73 Italian honey samples from six varieties (chestnut, eucalyptus, heather, sulla, honeydew, and wildflower) have been processed by Linear Discriminant Analysis (LDA), using two different variable selection procedures (Fisher F-based and stepwise LDA). Three and two variables, respectively have been necessary to obtain a 100% predictive ability as evaluated by cross-validation. Successively, a class modeling approach has been followed, using UNEQ. The resulting models showed 100% sensitivity and specificity.     

Discrimination of Ganoderma lucidum according to geographical origin with near infrared diffuse reflectance spectroscopy and pattern recognition techniques

A rapid and nondestructive near infrared (NIR) method combined with chemometrics was used to discriminate Ganoderma lucidum according to cultivation area. Raw, first, and second derivative NIR spectra were compared to develop a robust classification rule. The chemical properties of G. lucidum samples were also investigated to find out the difference between samples from six varied origins. It could be found that the amount of polysaccharides and triterpenoid saponins in G. lucidum samples was considerably different based on cultivation area. These differences make NIR spectroscopic method viable. Principal component analysis (PCA), discriminant partial least-squares (DPLS) and discriminant analysis (DA) were applied to classify the geographical origins of those samples. The results showed that excellent classification could be obtained after optimizing spectral pre-treatment. For the discriminating of samples from three different provinces, DPLS provided 100% correct classifications. Moreover, for samples from six different locations, the correct classifications of the calibration as well as the validation data set were 96.6% using the DA method after the SNV first derivative spectral pre-treatment. Overall, NIR diffuse reflectance spectroscopy using pattern recognition was shown to have significant potential as a rapid and accurate method for the identification of herbal medicines.     

Comparison of PLS1-DA, PLS2-DA and SIMCA for classification by origin of crude petroleum oils by MIR and virgin olive oils by NIR for different spectral regions

This study compares results obtained with several chemometric methods: SIMCA, PLS2-DA, PLS2-DA with SIMCA, and PLS1-DA in two infrared spectroscopic applications. The results were optimized by selecting spectral ranges containing discriminant information. In the first application, mid-infrared spectra of crude petroleum oils were classified according to their geographical origins. In the second application, near-infrared spectra of French virgin olive oils were classified in five registered designations of origins (RDOs). The PLS-DA discrimination was better than SIMCA in classification performance for both applications. In both cases, the PLS1-DA classifications give 100% good results. The encountered difficulties with SIMCA analyses were explained by the criteria of spectral variance. As a matter of fact, when the ratio between inter-spectral variance and intra-spectral variance was close to the F_c (Fisher criterion) threshold, SIMCA analysis gave poor results. The discrimination power of the variable range selection procedure was estimated from the number of correctly classified samples.     

A useful approach for the differentiation of wines according to geographical origin based on global volatile patterns

In this study, the feasibility of solid‐phase extraction combined with gas chromatography and mass spectrometry in tandem with partial least squares discriminant analysis was evaluated as a useful strategy to differentiate wines according to geographical origin (Azores, Canary and Madeira Islands) and types (white, red and fortified wine) based on their global volatile patterns. For this purpose, 34 monovarietal wines from these three wine grape‐growing regions were investigated, combining the high throughput extraction efficiency of the solid‐phase extraction procedure with the separation and identification ability. The partial least squares discriminant analysis results suggested that Madeira wines could be clearly discriminated from Azores and Canary wines. Madeira wines are mainly characterized by 2‐ethylhexan‐1‐ol, 3,5,5‐trimethylhexan‐1‐ol, ethyl 2‐methylbutanoate, ethyl dl ‐2‐hydroxycaproate, decanoic acid, 3‐methylbutanoic acid, and (E)‐whiskey lactone, whereas 3‐ethoxypropan‐1‐ol, 1‐octen‐3‐ol, (Z)‐3‐hexenyl butanoate, 4‐(methylthio)‐1‐butanol, ethyl 3‐hydroxybutanoate, isoamyl lactate, 4‐methylphenol, γ‐octalactone and 4‐(methylthio)‐1‐butanol, are mainly associated with Azores and Canary wines. The data obtained in this study revealed that solid‐phase extraction combined with gas chromatography and quadrupole mass spectrometry data and partial least squares discriminant analysis provides a suitable tool to discriminate wines, both in terms of geographical origin as well as wine type and vintage.     

Differentiation of young red wines based on cultivar and geographical origin with application of chemometrics of principal polyphenolic constituents

Nineteen major polyphenolic phytochemicals including hydroxycinnamate derivatives, flavanols, flavonols, and anthocyanins, were determined in 40 experimental red wines employing HPLC-DAD. All wines analysed were young (non-aged), produced, and stored under identical conditions, in an effort to minimize the effect of oak wood and vinification technology. The data obtained from this examination composed the matrix for the implementation of chemometrics, which aimed at differentiating the wine samples on the basis of cultivar and geographical region of origin. Discriminant analysis performed at a 95% significance level revealed a very satisfactory categorization of the samples both in terms of cultivar and region of origin, thus illustrating the validity of major phenolic phytochemicals for studies pertaining to wine quality and authenticity.     

Chemometrical strategies for feature selection and data compression applied to NIR and MIR spectra of extra virgin olive oils for cultivar identification

The possibility provided by Chemometrics to extract and combine (fusion) information contained in NIR and MIR spectra in order to discriminate monovarietal extra virgin olive oils according to olive cultivar (Casaliva, Leccino, Frantoio) has been investigated.Linear discriminant analysis (LDA) was applied as a classification technique on these multivariate and non-specific spectral data both separately and jointly (NIR and MIR data together).In order to ensure a more appropriate ratio between the number of objects (samples) and number of variables (absorbance at different wavenumbers), LDA was preceded either by feature selection or variable compression. For feature selection, the SELECT algorithm was used while a wavelet transform was applied for data compression.Correct classification rates obtained by cross-validation varied between 60% and 90% depending on the followed procedure. Most accurate results were obtained using the fused NIR and MIR data, with either feature selection or data compression.Chemometrical strategies applied to fused NIR and MIR spectra represent an effective method for classification of extra virgin olive oils on the basis of the olive cultivar.     

Characterization of the alcoholic fraction of vegetable oils by derivatization with diphenic anhydride followed by high-performance liquid chromatography with spectrophotometric and mass spectrometric detection

Aliphatic and triterpene alcohols present in vegetable oils have been identified and determined by HPLC using UV–vis and MS detection after previous derivatization with diphenic anhydride. The alcoholic fraction was obtained by saponification, extraction and TLC (according to the European Union official procedure). Derivatization was performed in tetrahydrofuran in the presence of suspended grinded urea, which increases the reaction rate and yield. Derivatized extracts were chromatographed on a C8 column using gradient elution with acetonitrile/water mixtures containing 0.1% acetic acid, with UV–vis followed by negative-ion mode MS detection. Using linear discriminant analysis of the HPLC-MS data (extracted ion chromatograms), oil samples belonging to seven botanical origins (hazelnut, sunflower, corn, extra virgin olive, soybean, peanut and grapeseed) were correctly classified with excellent resolution among all the categories.     

Nano and rapid resolution liquid chromatography–electrospray ionization–time of flight mass spectrometry to identify and quantify phenolic compounds in olive oil

The applicability of nanoLC‐ESI‐TOF MS for the analysis of phenolic compounds in olive oil was studied and compared with a HPLC method. After the injection, the compounds were focused on a short capillary trapping column (100 μm id, effective length 20 mm, 5 μm particle size) and then nanoLC analysis was carried out in a fused silica capillary column (75 μm id, effective length 10 μm, 3 μm particle size) packed with C18 stationary phase. The mobile phase was a mixture of water + 0.5% acetic acid and ACN eluting at 300 nL/min in a gradient mode. Phenolic compounds from different families were identified and quantified. The quality parameters of the nanoLC method (linearity, LODs and LOQs, repeatability) were evaluated and compared with those obtained with HPLC. The new methodology presents better sensitivity (reaching LOD values below 1 ppb) with less consumption of mobile phases, but worse repeatability, especially inter‐day repeatability, resulting in more difficulties to get highly accurate quantification. The results described in this article open up the application fields of this technique to cover a larger variety of compounds and its advantages will make it especially useful for the analysis of samples containing low concentration of phenolic compounds, as for instance, in biological samples.