Development of a voltammetric electronic tongue for discrimination of edible oils

In this paper, we propose a novel strategy to perform cyclic voltammetric measurements with a platinum microelectrode directly in edible oil samples. The microelectrode was employed as an electronic tongue that, along with the application of chemometrics to the current–potential responses, proved useful for discriminating oils on the basis of their quality and geographical origin. The method proposed here is based on the use of suitable room temperature ionic liquids, added to oils as supporting electrolytes to provide conductivity to the low-polarity samples. The entire voltammograms, recorded directly on the oil/RTIL mixtures, were processed via principal component analysis and a classification technique (K nearest neighbors), to extract information on samples characteristics. Data processing showed that oils having different nature (i.e. maize and olive) or geographical origin (i.e. olive oils coming from different regions) can be distinguished.     

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 and classification of Western Greek olive oils according to cultivar and geographical origin based on volatile compounds

The aim of the present study was to characterize and classify olive oils from Western Greece according to cultivar and geographical origin, based on volatile compound composition, by means of Linear Discriminant Analysis. A total of 51 olive oil samples were collected during the harvesting period 2007-2008 from six regions of Western Greece and from six local cultivars. Forty-five of the samples were characterized as extra virgin olive oils. The analysis of volatile compounds was performed by Headspace Solid Phase Microextraction-Gas Chromatography/Mass Spectrometry (HS-SPME-GC/MS). Fifty-three (53) different volatile compounds were tentatively identified and semi-quantified. Using selected volatile compound composition data (selection was based on the application of ANOVA to total volatiles to determine those variables showing substantial differences among samples of different geographical origin/cultivar), the olive oil samples were satisfactorily classified according to geographical origin (87.2%) and cultivar (74%).     

Territorial origin of olive oil: representing georeferenced maps of olive oils by NMR profiling

ABSTRACT Proton NMR profiling is nowadays a consolidated technique for the identification of geographical origin of food samples. The common approach consists in correlating NMR spectra of food samples to their territorial origin by multivariate classification statistical algorithms. In the present work, we illustrate an alternative perspective to exploit territorial information, contained in the NMR spectra, which is based on the implementation of a geographic information system (GIS). Nuclear magnetic resonance spectra are used to build a GIS map permitting the identification of territorial regions having strong similarities in the chemical content of the produced food (terroir units). These terroir units can, in turn, be used as input for labeling samples to be analyzed by traditional classification methods. In this work, we describe the methods and the algorithms that permit to produce GIS maps from NMR profiles and apply the described method to the analysis of the geographical distribution of olive oils in an Italian region. In particular, we analyzed by ¹H NMR up to 98 georeferenced olive oil samples produced in the Abruzzo Italian region. By using the first principal component of the NMR variables selected according to the Moran test, we produced a GIS map, in which we identified two regions incidentally corresponding to the provinces of Teramo and Pescara. We then labeled the samples according to the province of provenience and built an LDA model that provides a classification ability up to 99% . A comparison between the variables selected in the geostatistics and classification steps is finally performed. Copyright © 2016 John Wiley & Sons, Ltd.     

NMR and chemometrics in tracing European olive oils: The case study of Ligurian samples

An NMR and chemometric analytical approach to classify extra virgin olive oils according to their geographical origin was developed within the European TRACE project (FP6-2003-FOOD-2-A, contract number: 0060942). Olive oils (896 samples) of three consecutive harvesting years (2005, 2006, and 2007) coming from Mediterranean areas were analyzed by ¹H NMR spectroscopy. Olive oil samples from Liguria, an Italian region, were chosen as a case study and PLS-DA and SIMCA modeling analyses were used to build up statistical models both to discriminate between Ligurian and non-Ligurian olive oils and to define the Ligurian olive oil class to confirm the declared provenience.     

Study of quantitative and qualitative variations in essential oils of Sicilian Rosmarinus officinalis L.

In this study the chemical characterisation of 10 Sicilian Rosmarinus officinalis L. biotypes essential oils is reported. The main goal of this work was to analyse the relationship between the essential oils yield and the geographical distribution of the species plants. The essential oils were analysed by GC-FID and GC-MS. Hierarchical cluster analysis and principal component analysis statistical methods were used to cluster biotypes according to the essential oils chemical composition. The essential oil yield ranged from 0.8 to 2.3 (v/w). In total 82 compounds have been identified, these represent 96.7–99.9% of the essential oil. The most represented compounds in the essential oils were 1.8-cineole, linalool, α-terpineol, verbenone, α-pinene, limonene, bornyl acetate and terpinolene. The results show that the essential oil yield of the 10 biotypes is affected by the environmental characteristics of the sampling sites while the chemical composition is linked to the genetic characteristics of different biotypes.     

Geographic characterization of Greek wine by inductively coupled plasma–mass spectrometry macroelemental analysis

Abstract

Studying wine mineral profile has been proven as a valuable tool in geographical origin discrimination and authenticity for both producers and consumers. Adulteration of wines, in terms of geographical origin or variety, is considered a major topic of extensive research. Traceability and authenticity of wines have been previously studied on the basis of typical mineral element patterns by means of chemometric methods. In this context, analytical methods were developed for the determination of mineral elements in wines by inductively coupled plasma–mass spectrometry. This study aimed at classifying selected varietal Greek wines from various regions by employing instrumental analysis. Preliminary data of wine mineral content enabled for the classification of samples according to geographical origin and variety. However, further work is required in order to draw more valid conclusions and to obtain a detailed map of the mineral element content of Greek wines according to their geographical origin and/or variety.     

Monoterpene and sesquiterpene hydrocarbons of virgin olive oil by headspace solid-phase microextraction coupled to gas chromatography/mass spectrometry

In the present article, a headspace solid-phase microextraction method coupled to GC/MS was developed and applied for the simultaneous determination of mono- and sesquiterpenic hydrocarbons in virgin olive oils of different olive variety and geographical origin. Analysis of various oils resulted in the simultaneous detection of 15 monoterpenes and 30 sesquiterpenes. Some of these hydrocarbons were previously reported to be constituents of virgin olive oil terpenoid fraction, although we also detected some terpenic hydrocarbons that have not previously been documented as present in virgin olive oil. Significant differences were detected in the proportion of terpenic compounds in oils obtained from different olive varieties grown in different geographical areas. The monoterpene, and particularly the sesquiterpene composition of olive oil may be used to distinguish samples from different cultivar and geographical areas.     

The information content of visible spectra of extra virgin olive oil in the characterization of its origin

The information content of visible spectra has been evaluated, by means of some selected chemometrical techniques, for its ability to trace the geographical origin of extra virgin olive oils coming from several Mediterranean regions. Special attention was paid to extra virgin olive oil produced in West Liguria, a North Italy region which leans over the Mediterranean Sea and borders France. The peculiar organoleptic features of this "niche product" deserved the protected designation of origin "Riviera Ligure-Riviera dei fiori". Unfortunately, this expensive oil is often submitted to profitable adulterations, commonly involving addition of other cheaper Mediterranean oils. Using suitable transforms, such as profiles and derivatives, the visible spectra of extra virgin olive oils showed a very important discriminant power in that regards the geographical characterization of the studied samples. In particular, the developed class models for West Liguria oils have 100% sensitivity and specificity. Moreover, even if this paper is focused on West Liguria oil, it is important to emphasize that a similar study, involving a so widespread and timesaving technique, could be analogously developed for all the other Mediterranean regions taken into account and it could be used in other olive oil characterization problems.     

Chemometrical applications in an industrial food research laboratory

Early typical chemometrical applications in oils and fats research concernedpattern recognition problems using multivariate analysis (principal component analysis, discriminant analysis, canonical variates). Various types of fish oils can now be quickly allocated with respect to their origin. Fuzzy set theory was used in a different approach to classification applied to the allocation of yellow fat spreads into product categories using sensory attributes scored on a truth scale. Partial least-squares technique has found practical applications in problems of multivariate calibration, sensory analysis and quantitative structure-activity relationships. Also the theoretical aspects of PLS regression have been investigated, in particular the underlying optimization criterion and the relation to other multivariate techniques. Mixed integer programming has been helpful in identifying and quantifying the oil composition of unknown fat blends from their fatty acid profiles, improving upon an earlier constrained regression technique using brute force all-possible subset selection.     

Characterization of fresh and aged natural ingredients used in historical ointments by molecular spectroscopic techniques: IR,Raman and fluorescence

Natural organic materials used to prepare pharmaceutical mixtures including ointments and balsams have been characterized by a combined non-destructive spectroscopic analytical approach. Three classes of materials which include vegetable oils (olive, almond and palm tree), gums (Arabic and Tragacanth) and beeswax are considered in this study according to their widespread use reported in ancient recipes. Micro-FTIR, micro-Raman and fluorescence spectroscopies have been applied to fresh and mildly thermally aged samples. Vibrational characterization of these organic compounds is reported together with tabulated frequencies, highlighting all spectral features and changes in spectra which occur following artificial aging. Synchronous fluorescence spectroscopy has been shown to be particularly useful for the assessment of changes in oils after aging; spectral difference between Tragacanth and Arabic gum could be due to variations in origin and processing of raw materials. Analysis of these materials using non-destructive spectroscopic techniques provided important analytical information which could be used to guide further study.     

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.     

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.     

Geographical traceability of West Liguria extravirgin olive oils by the analysis of volatile terpenoid hydrocarbons

This study aimed at evaluating if the volatile terpenoid hydrocarbons of extravirgin olive oils from West Liguria, a North Italy region, could trace their geographical origin. If terpenoid hydrocarbons were individually considered, three compounds, i.e. alpha-copaene, alpha-muurolene and alpha-farnesene, allowed building a simple decision tree and discriminating oils produced in West Liguria from oils produced in other Mediterranean regions. Moreover, the multivariate analysis allowed building West Liguria class-models with high predictive ability, confirming the fundamental role of the volatile terpenoid hydrocarbons for the geographical characterisation of West Liguria oils.     

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.     

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.]     

Comparative study of artificial neural network and multivariate methods to classify Spanish DO rose wines

Classical multivariate analysis techniques such as factor analysis and stepwise linear discriminant analysis and artificial neural networks method (ANN) have been applied to the classification of Spanish denomination of origin (DO) rose wines according to their geographical origin. Seventy commercial rose wines from four different Spanish DO (Ribera del Duero, Rioja, Valdepeñas and La Mancha) and two successive vintages were studied. Nineteen different variables were measured in these wines. The stepwise linear discriminant analyses (SLDA) model selected 10 variables obtaining a global percentage of correct classification of 98.8% and of global prediction of 97.3%. The ANN model selected seven variables, five of which were also selected by the SLDA model, and it gave a 100% of correct classification for training and prediction. So, both models can be considered satisfactory and acceptable, being the selected variables useful to classify and differentiate these wines by their origin. Furthermore, the casual index analysis gave information that can be easily explained from an enological point of view.     

Mass spectrometry fingerprinting coupled to National Institute of Standards and Technology Mass Spectral search algorithm for pattern recognition

A new analytical strategy based on mass spectrometry fingerprinting combined with the NIST-MS search program for pattern recognition is evaluated and validated. A case study dealing with the tracing of the geographical origin of virgin olive oils (VOOs) proves the capabilities of mass spectrometry fingerprinting coupled with NIST-MS search program for classification. The volatile profiles of 220 VOOs from Liguria and other Mediterranean regions were analysed by secondary electrospray ionization-mass spectrometry (SESI-MS). MS spectra of VOOs were classified according to their origin by the freeware NIST-MS search v 2.0. The NIST classification results were compared to well-known pattern recognition techniques, such as linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLS-DA), k-nearest neighbours (kNN), and counter-propagation artificial neural networks (CP-ANN). The NIST-MS search program predicted correctly 96% of the Ligurian VOOs and 92% of the non-Ligurian ones of an external independent data set; outperforming the traditional chemometric techniques (prediction abilities in the external validation achieved by kNN were 88% and 84% for the Ligurian and non-Ligurian categories respectively). This proves that the NIST-MS search software is a useful classification tool.     

Classification of vegetable oils according to their botanical origin using amino acid profiles established by direct infusion mass spectrometry

Amino acid profiles, established by direct infusion mass spectrometry, have been used to classify vegetable oils according to their botanical origin. The proteins present in hazelnut, sunflower, corn, soybean, olive, avocado, peanut and grapeseed oils were precipitated with acetone, and the residue was hydrolyzed in acid medium, diluted in a hydrochloric acid/ethanol mixture, and infused into the mass spectrometer. The spectra of the hydrolyzed protein extracts showed [M+H]+ ions of the following amino acids: glycine, alanine, serine, proline, valine, threonine, cysteine, isoleucine + leucine, aspartic acid, lysine, glutamic acid, methionine, histidine, phenylalanine, arginine and tyrosine. These ions were used to construct linear discriminant analysis (LDA) models. The ratios of the ion signal intensities selected by pairs were used as predictors. With the sequential application of three LDA models, the eight botanical origin categories of the samples were well resolved.