Browsing by Author "Epova, Ekaterina"
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- Multielement fingerprinting, isotope ratios and chemometrics as tools to trace the geographical origin of winePublication . Coelho, Inês; Matos, Ana Sofia; Nascimento, Ana; Bordado, João; Donard, Olivier; Epova, Ekaterina; Barre, Julien; Berali, Sylvain; Castanheira, IsabelThe aim of the present work is to develop a chemical fingerprint using multielement analysis and isotope ratios, for tracing the geographical origin of Douro and Port wines. Both wines are produced within the Douro region, located in northeastern Portugal. Although the terroir is the same the fermentation of Port Wine is interrupted with the addition of “Aguardente Vínica” (alcohol distilled from wine) which alters the sweetness of the wine according to when it is added. Thirty samples of wine were supplied by the Instituto dos Vinhos do Douro e do Porto. Samples were digested using closed vessel microwave digestion. Multielement analyses were carried out, in triplicate, in compliance with NP EN ISO/IEC 17025. Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was used for the determination of 25 elements (Li, Be, B, Al, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Mo, Cd, Sn, Tl, Pb, Fe, Mg, Ca, Na, P and K). The digests were further purified for strontium isolation and determination of 87Sr/86Sr ratio by Mulitcollector ICP-MS. 18Oxygen was determined by Isotope Ratio Mass Spectrometry (IRMS). Results showed that all legislated elements (B, As, Pb, Zn, Cu and Cd) complied with maximum admissible values. Cd and Tl were removed from the statistical analysis since results were below the limit of quantification in all samples. Principal Component analysis (PCA) was applied to the remaining samples. This unsupervised method returned 5 principal components (PCs) that explained 82% of total variance. Twelve parameters (Li, B, Rb, K, 87Sr/86Sr, Cr, Ni, Cu, Pb, Fe, Sr, Mn) showed influence in the PCs and therefore were used for Hieraquical Cluster Analysis (HCA). Based on the results from HCA a predictive model using Linear Discriminant Analysis (LDA) was constructed. Samples were chosen either for building the LDA model or as cross validation data, according to the results obtained in the HCA. According to the obtained results it is possible to distinguish between Port and Douro wines based on elemental inorganic analysis and Sr isotope ratios. However, the choice of samples used for the construction of the predictive model is crucial for correct identification. Data was produced in compliance with quality requirements therefore being adequate to integrate Food Composition Databases.
- Tracing the geographical origin of food products with multielement fingerprinting, isotope ratios and chemometrics: the case of Rocha pearPublication . Coelho, Inês; Matos, Ana Sofia; Nascimento, Ana; Bordado, João; Donard, Olivier F.X.; Epova, Ekaterina; Berail, Sylvain; Castanheira, IsabelRocha pear (Pyrus communis L. var. Rocha) is a traditional Portuguese variety of pear recognized by its organoleptic qualities. Adding to the known benefits of consuming fruit in general, pears are considered a good source of fiber and potassium with low sodium content, combined with low glycemic index (Barda, 2011; Foster-Powell et al., 2002) . The nutritional value of pears, in particular its mineral content, is of great relevance for human nutrition and public health. In this sense, Food Composition Databases (FCDBs) are a crucial tool. In this study, we analyze the mineral content of Rocha pears to promote the linkage between the nutritional profile and geographical origin, filling the gap of absent values in FCDBs. Rocha pears produced in the west part of Portugal can be certified as Protected Designation of Origin (PDO), under the label of “Pera Rocha do Oeste”. These represent the largest PDO production in the country (Globalagrimar, 2016). However, this variety is also cultivated in other areas at national level (nPDO) and is gaining interest at international level due to growing exportations. The mineral profile of plants is a reflection of the soil type and the environmental conditions under which plants were grown. Therefore, minerals and trace elements, as well as their isotopes, are expected to be suitable biomarkers in authenticity studies (Coelho et al., 2017; Gonzalvez et al., 2009). Nonetheless, to extract the most information from datasets these must to be combined with chemometric tools capable of performing multivariate data analysis. The present work aims to develop a chemical fingerprint of Rocha pears produced within the PDO region based on multielement analysis and isotope ratios (IR). For such purpose 150 pear samples were collected from 10 farms located in two regions (PDO and nPDO). Samples were analyzed in pools, each composed of 3 pears from the same tree, leading to 50 pools. Multielement analyses were carried out, in triplicate, in compliance with NP EN ISO/IEC 17025 and EN 1380. The content of twenty-four minerals and trace elements was measured by 2 inductively coupled plasma optical emission spectrometer (ICP-OES) (Fe, Mg, P, Ca, Na, K) and ICP mass spectrometer (ICP-MS) (Li, Be, B, Al, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Sr, Mo, Cd, Sn, Tl, Pb). A Multicollector-ICP-MS (MC-ICP-MS) was used to determine Sr IR. Results showed a low level of the analyzed contaminants indicating that these do not present a risk for consumers. K was the most abundant element, followed by P. Be, Se, Sn and Pb were systematically below the limit of quantification (LOQ) and were not included in the statistical treatment. The dataset for multielement content and Sr IR was analyzed resorting to chemometric techniques, namely Principal Component Analysis (PCA), Hierarchical Clustering Analysis (HCA) and Linear Discriminant Analysis (LDA). Differences were identified in the level of inorganic components between the regions under study. The most significant were in the levels of Al, Ni and Na, which were higher in samples originating from the PDO region, while the content of Sr and Tl were superior outside this region. The exploratory analysis, based on the unsupervised models PCA and HCA, identified relationships between samples and their geographical provenance. LDA confirmed the possibility of tracing pear samples to the correspondent origin based on mineral profile and Sr IR. Samples used for cross validation of the predictive model were matched to its origin with 100% accuracy. In conclusion, the analytical methods were suitable for the purpose and are recommended methods for the analysis of inorganic components even at low values. The chemometric techniques used allowed the differentiation of samples according to their geographical origin, contributing to its authenticity and traceability as well as to a deeper knowledge of the mineral content of Rocha pear. The combination of these two techniques is a proper tool for mineral fingerprinting of Rocha Pear’s geographical origin.