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Geographical origin identification of Romanian wines by ICP-MS elemental analysis
Food Chemistry 138 (2013) 1125–1134
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Geographical origin identification of Romanian wines by ICP-MS elemental analysis Irina Geana a, Andreea Iordache a, Roxana Ionete a, Adrian Marinescu a, Aurora Ranca b, Monica Culea c,⇑ a
National R&D Institute for Cryogenics and Isotopic Technologies’ – ICIT Rm. Valcea, 4 Uzinei St, 240050 Rm. Valcea, Romania Research Station for Viticulture and Oenology Murfatlar, Murfatlar 905100, Romania c Babes-Bolyai’ University, 1 M. Kogalniceanu St, Cluj-Napoca 400084, Romania b
a r t i c l e
i n f o
Article history: Received 4 July 2012 Received in revised form 9 October 2012 Accepted 21 November 2012 Available online 5 December 2012 Keywords: Trace elements Wine Authentication Acid extraction ICP-MS Geographical origin
a b s t r a c t Trace elemental analysis, besides its ability to determine stable isotopes ratios, represents a possible complementary tool useful to differentiate wines based on their regional origins. Wines and their provenance soils from two major wine producing areas in Southeast Romania (‘Valea Calugareasca’ and ‘Murfatlar’), and also wine from the region of Moldova (Eastern Romania) were analyzed by inductively coupled plasma mass spectrometry (ICP-MS), and statistical data of elemental composition was used to differentiate these wines according to grape type and geographical origin. Moreover, this study gathers relevant elemental trace composition of wines produced in most important Romanian vineyards, thus offering a useful wine differentiation tool by their production district. The results show that the differentiation of Romanian wines according to their provenance is based on the following main elements: Ni, Ag, Cr, Sr, Zn, and Cu for Valea Calugareasca, Rb, Zn, and Mn for Murfatlar, and Pb, Co, and V for Moldova. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Geographical origin and authenticity are both factors influencing the overall perception of wines in terms of quality, and price, hence, being of great importance to consumers and commercial wine producers. Multi elemental trace analysis and rare earth element analysis by spectroscopic methods, such as AAS or ICP-MS, are valuable tools for identifying the origin of a wine (Almeida & Vasconcelos, 2003; Coetzee et al., 2005). Given the fact that the natural diffusional movement of elemental traces follows a pattern, moving from rocks to soil, and from the soil to the grape, allows for wines to be differentiated through the elemental analysis of their provenance soils. Besides the ‘natural’ mineral content of the soil and the grape vine’s capacity to uptake and accumulate these elements in the grape berry, there are several other variables such as environmental contamination, agricultural practices, or wine-making treatments with metal containing products (e.g. bentonite addition in the wine clarifying step) altering the final composition, and concentration of minerals in wines. These factors may markedly affect the elemental composition of wines, and render the correlation between the wine and its provenance soil to be of a more complex nature (La Pera et al., 2008; Mihucz et al., 2006; Suhaj & Korenovska, 2005).
⇑ Corresponding author. Tel.: +40 264593833x5712; fax: +40 264590818. E-mail addresses: [email protected] (I. Geana), [email protected] (A. Iordache), [email protected] (R. Ionete), [email protected] (A. Marinescu), [email protected] (A. Ranca), [email protected] (M. Culea). 0308-8146/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodchem.2012.11.104
The identification of major and trace elements in wines from different regions of the world with the aim to survey the content of certain metals, and test the wine provenance, or region of origin, has been previously studied in countries like Spain (Álvarez, Moreno, Jos, Cameán, & González, 2007; Gonzálvez, Llorens, Cervera, Armenta, & De La Guardia, 2009; Jos, Moreno, González, Repetto, & Cameán, 2004), Czech Republic, Slovakia (Korenovska & Suhaj, 2005), Hungary (Sass-Kiss, Kiss, Havadi, & Adanyi, 2008), Switzerland (Gremaud, Quaile, Piantini, Pfammatter, & Corvi, 2004), South Africa (Van Der Linde, Fischer, & Coetzee, 2010), Germany, Italy, Portugal, and France (Giaccio & Vicentini, 2008). Wines typically contain macro-elements such as Na, K, Mg, Ca (c > 10 mg/l), micro-elements such as Fe, Cu, Zn, Mn, Pb (c > 10 lg/ l), and ultra microelements such as Cr, As, Cd, and Ni (c < 10 lg/l) (Voica, Dehelean, & Pamula, 2009). Certain macro and micronutrients, such as Na, K, Ca, Fe, Cu, and Zn, show significant changes during the technological processes involved in wine making and are a reason for the attention given to the elements which show very small variation, although they are found in small quantities or only in trace amounts (e.g. Cr, Co, Sb, Cs, Sc, Eu, Hf, Tl, etc.). Trace elements less affected by the wine making processes are the alkaline earth metals, of which Li and Rb are the most relevant for geographical origin identification. According to earlier works, the most frequently quantified and cited elements used for wine authentication are K, Na, Fe, Y, Rb, Ca, Cu, Cr, Co, Sb, Cs, Br, As, Ag, Li, Ba, Sr, Mg, Al and Mn (Arvanitoyannis, Katsota, Psarra, Soufleros, & Kallithraka, 1999; Medina, 1996). For Romania, according to studies performed to date, the main elements allowing a differentiation between wines are Mn and Sr (Suhaj & Korenovska, 2005).
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Table 1 The program of the microwave furnace Mars 5 Microwave System for wine extractions. Step
1 2
Power Level
%
600 15 min-cooling
100
Ramp time (min)
Pressure (psi)
Temperature (°C)
Hold time (min)
10
800
180
20
Ramp time (min)
Pressure (psi)
Temperature (°C)
Hold time (min)
6
800
200
20
Table 2 Digestion program for soil extractions. Step
1 2
Power Level
%
800 15 min-cooling
100
Since metal ions play significant roles in the wine making process (e.g. oxidation reactions, metabolism of wine yeast and bacteria) and, to our knowledge, metal ions compositional information used to authenticate Romanian wines is scarce, we focused our attention on three of the main wine regions covering some of the famous Romanian viticultural areas, although Romania is divided into 8 wine regions, and 37 areas of production (33 red wines, and 67 white wines) (Schlesier et al., 2009). The aim of the present paper is to investigate the metal content profile (considering the elements Cr, Ni, Rb, Sr, Ag, Zn, Mn, Cu, Co, V, Pb, and Be) of authentic wine samples from several famous Romanian vineyards of Muntenia (Valea Calugareasca vineyard), Dobrogea (Murfatlar) and Moldova (Iasi, Cotnari, Bujoru, Panciu, Odobesti, Nicoresti) in an attempt to differentiate wines by region depending on their elemental composition. A critical factor for choosing the appropriate analytical method for the elemental fingerprinting of wines was the multi-element detection capability, with ICP-MS being a suitable technique for accurate and fast determination of trace and ultra-trace elements in the same sample. We further statistically analyzed the distribution of elements in the soils of studied regions, and the correlation between the elemental composition of analyzed wines and their provenance soil was substantiated. 2. Materials and methods 2.1. Apparatus and reagents Analytical measurements were performed using an inductively coupled plasma mass spectrometer (ICP-MS Varian 820-MS from Varian, Australia) equipped with an SPS-3 autosampler (Varian, Australia), a micro-concentric nebulizer, nickel cones, and a peristaltic sample delivery pump, running a quantitative analysis mode. Each sample was analyzed in duplicate, and each analysis consisted of five replicates. The gaseous argon used to form the plasma in the ICP-MS was of purity 6.0 (Messer, Austria).
A Mars 5 Microwave System (CEM Microwave Technology Ltd, UK) equipped with a microwave acid digestion bomb made from Teflon, with a capacity of 100 ml was used for microwave digestion. High purity ICP Multi Element Standard Solution XXI CertiPUR obtained from Merck (Darmstadt, Germany) was used for the calibration curve in the quantitative analysis. The ICP Multi Element Standard Solution XXI CertiPUR was a mixture of 10.0 mg/l of As, Be, Bi, Co, Cr, Cu, K, Li, In, Tl, Se, Rb and V, 10.1 mg/l of U, Mg, Ni, and Ba and 9.9 mg/l of Al, Cd, Fe, Ag, Ni and Zn. HNO3 69% (w/v), concentrated HF and HCl, reagent grade from Merck and ultrapure water with a maximum resistivity of 18.2 M X cm 1 , obtained from a Milli-Q Millipore system (Bedford, MA, USA) were used for sample treatment and sample dilution. 2.2. Wine and soil samples For this study, authentic wine samples from different vineyards located in some of the most important Romanian wine regions, namely the Dobrogea region (Murfatlar vineyard), Muntenia region (Valea Calugareasca vineyard) and Moldova region (Iasi, Cotnari, Panciu, Odobesti, Nicoresßti and Bujoru vineyards) were investigated. The wine samples were obtained by microvinification based on EC Regulation No. 2729/2000, consolidated with EC Regulation No. 2030/2006. A set of 60 wine samples were collected from the three most important Romanian wine regions. Of these, 26 were red wines. 20 samples were from Valea Calugareasca vineyard, 18 samples from Murfatlar vineyard and 22 from the Moldova Region. Most samples collected (36) were from the harvest of 2010, while the remaining 24 samples were from the harvest of 2009 (samples for checking the stability of measured parameters), and all samples were of high quality. The samples represent eighteen grape varieties, from native (Feteasca Regala – FR, Feteasca Alba – FA, Mamaia – MM, Columna – CO, Tamaioasa Romaneasca – TR, Grasa de Cotnari – GR, Babeasca Neagra – BN, Feteasca Neagra – FN,
Table 3 Instrumental (a) and data acquisition (b) parameters of ICP-MS. (a) Instrumental parameters RF power Argon gas flow Nebulizer Plasma Lens voltage Mirror lens left Mirror lens right Mirror lens bottom Sample uptake rate
(b) Data acquisition parameters for quantitative mode 1.4 kW 1. L/min 18.0 L/min 37 V 31 V 30 V 40 s
Measuring mode Point per peak Scans/Replicate Replicate/Sample Dwell time (ms)
Peak hopping 3 10 10 1
Integration time (s)
395.08 s
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Francusa – FC), to foreign origin (Saugvinon – S, Pinot Gris – PG, Chardonnay – CH, Riesling Italian – RI, Cabernet Saugvinon – CS, Pinot Noir – PN, Merlot – ME, Burgund Mare – BM, Muscat Ottonel – MO), both red and whites, were analyzed. Their ethanol contents ranged from 11% to 14.8% v/v.
The plastic containers used for storing and treating the samples were cleaned to avoid contamination of samples with traces of any metal. Containers were treated with nitric acid and then washed with ultra pure water. Once opened, wine samples were treated according to the preparation procedure.
Table 4 Experimental results of elements (lg/L) measured in wine samples and their geographical origin. Elements
Cr
Ni
Rb
Sr
Ag
Zn
Mn
Cu
Co
V
Pb
Be
All three investigated regions All wines (n = 60) Mean SD Min Max Median Red wines (n = 26) Mean SD Min Max Median White wines (n = 34) Mean SD Min Max Median
254.79 262.31 60.96 1725.80 181.64 233.00 176.96 109.58 982.91 195.68 269.32 308.00 60.96 1725.80 174.29
55.15 61.26 1.44 322.73 35.13 51.67 52.05 5.07 217.09 32.54 57.48 67.31 1.44 322.73 37.35
890.11 458.92 219.56 2066.21 781.45 1050.46 496.11 410.05 2066.21 843.01 783.20 404.84 219.56 1755.39 721.15
540.55 291.73 144.34 1600.68 471.28 675.06 310.23 353.02 1538.44 599.63 450.87 244.01 144.34 1600.68 408.37
7.01 2.92 0.55 12.16 7.44 7.21 3.39 0.86 12.16 8.21 6.87 2.60 0.55 11.26 7.22
433.95 265.16 57.44 1442.65 385.48 376.02 215.46 57.44 1004.79 317.89 472.57 290.14 178.50 1442.65 406.57
805.89 533.09 223.5 3077.75 604.79 915.90 571.01 280.05 3077.75 857.46 732.55 500.96 223.5 2454.49 562.60
500.57 466.48 25.99 2594.79 367.59 347.81 291.35 25.99 1216.71 250.13 602.41 533.26 82.06 2594.79 522.46
4.35 5.83 0.34 42.06 2.76 2.43 1.40 0.34 6.03 2.06 5.63 7.19 1.31 42.06 3.35
47.61 35.73 18.61 238.05 41.13 46.30 42.93 18.61 238.05 32.31 48.48 30.65 22.25 203.92 41.45
37.97 28.79 17.01 235.9 33.04 30.68 11.47 17.01 57.65 27.71 42.83 35.35 19.20 235.9 37.54
7.29 0.27 6.7 8.05 7.20 7.18 0.23 6.7 7.78 7.16 7.37 0.28 7.04 8.05 7.27
Muntenia region/Valea Calugareasca vineyard All wines (n = 20) Mean 299.69 SD 262.43 Min 113.13 Max 1101.09 Median 208.98 Red wines (n = 10) Mean 289.65 SD 250.62 Min 113.13 Max 982.91 Median 222.38 White wines (n = 10) Mean 309.73 SD 286.97 Min 134.73 Max 1101.09 Median 208.98
70.77 67.55 14.4 322.73 53.38 59.76 40.19 14.4 137.51 48.82 81.78 88.02 23.65 322.73 54.40
718.40 295.38 219.56 1440.54 682.47 886.12 304.75 560.54 1440.54 773.56 550.68 169.75 219.56 792.94 545.59
709.02 386.29 278.67 1600.68 570.69 823.23 387.36 475.22 1538.44 619.28 594.80 368.76 278.67 1600.68 514.03
8.86 1.48 6.78 12.16 8.43 9.22 1.66 7.47 12.16 8.49 8.50 1.26 6.78 11.20 8.37
442.53 239.08 248.22 1356.94 414.33 360.40 109.59 248.22 580.82 314.46 524.65 306.06 268.16 1356.94 453.44
437.68 160.01 223.5 952.5 447.94 501.31 183.76 280.05 952.50 466.67 374.06 106.25 223.5 506.55 367.71
703.38 528.36 148.9 2594.79 658.88 520.44 360.42 148.90 1216.71 401.76 886.31 620.55 450.41 2594.79 761.53
3.45 2.26 1.62 11.58 3.03 2.97 1.57 1.62 6.03 2.21 3.93 2.79 1.63 11.58 3.35
28.73 5.17 18.61 37.66 27.71 27.15 3.94 18.61 33.07 27.69 30.30 5.94 22.25 37.66 28.48
38.19 14.05 20.64 80.53 36.51 31.71 10.58 20.64 50.27 30.25 44.68 14.54 31.16 80.53 39.25
7.15 0.07 7.04 7.31 7.16 7.18 0.07 7.08 7.31 7.16 7.13 0.07 7.04 7.22 7.13
Dobrogea region/Murfatlar vineyard All wines (n = 18) Mean SD Min Max Median Red wines (n = 8) Mean SD Min Max Median White wines (n = 10) Mean SD Min Max Median
250.21 121.90 120.51 533.03 254.26 248.62 88.54 121.48 339.9 282.20 251.22 143.36 120.51 533.03 171.55
65.93 72.39 9.58 263.77 34.59 78.05 72.72 19.74 217.09 60.24 58.22 74.62 9.58 263.77 31.59
1125.92 592.46 346.32 2066.21 1116.07 1215.49 739.42 410.05 2066.21 1163.02 1068.92 510.08 346.32 1755.39 1072.16
504.80 207.00 225.32 808.61 449.48 598.53 192.95 360.66 796.42 613.75 445.16 201.11 225.32 808.61 434.05
3.64 2.83 0.55 7.01 1.91 2.83 2.62 0.86 6.64 1.30 4.16 2.96 0.55 7.01 6.58
476.05 338.07 181.53 1442.65 360.17 456.94 207.52 214.35 845.17 459.42 488.20 409.92 181.53 1442.65 336.09
1331.94 646.62 308.37 3077.75 1206.76 1468.67 711.64 1110.70 3077.75 1214.47 1244.92 620.91 308.37 2454.49 1173.05
240.41 183.29 25.99 724.71 201.15 163.23 129.40 25.99 367.02 103.84 289.52 200.60 82.06 724.71 270.77
4.56 9.55 0.34 42.06 1.85 2.10 1.56 0.34 5.04 2.11 6.21 12.11 1.31 42.06 1.64
48.77 11.49 27.33 65.56 54.16 50.33 12.01 27.33 65.56 53.79 47.78 11.62 30.35 58.95 54.52
33.15 5.97 24.74 44.42 31.81 35.07 7.50 25.14 44.42 34.70 31.93 4.76 24.74 41.92 31.05
7.43 0.41 6.7 8.05 7.47 7.23 0.42 6.7 7.78 7.20 7.56 0.36 7.04 8.05 7.62
217.72 339.99 60.96 1725.80 133.22 136.44 38.31 109.58 219.86 122.37 255.66 409.78 60.96 1725.80 147.78
32.14 35.86 1.44 127.05 18.97 13.72 8.70 5.07 27.21 11.01 40.73 40.59 1.44 127.05 28.88
853.26 389.16 262.49 1715.68 847.31 1120.20 419.67 743.40 1715.68 866.29 728.69 315.33 262.49 1351.14 773.42
416.64 158.73 144.34 855.37 368.04 539.93 204.67 353.02 855.37 473.89 359.10 93.23 144.34 529.85 334.49
8.07 1.15 6.65 10.47 7.64 8.73 1.11 7.41 10.47 8.53 7.76 1.06 6.65 10.18 7.38
391.72 223.27 57.44 1004.79 383.09 317.42 323.76 57.44 1004.79 305.95 426.40 160.63 178.50 940.98 400.81
710.23 246.76 292.73 1403.58 643.96 955.39 219.15 768.14 1403.58 862.80 595.81 163.32 292.73 904.98 595.63
529.06 483.28 176.00 2515.37 417.09 285.77 141.02 176.00 512.52 204.43 642.60 546.26 285.00 2515.37 537.04
4.99 3.96 1.20 17.39 3.70 1.98 0.75 1.20 3.41 1.99 6.40 4.08 2.08 17.39 6.10
63.82 53.30 26.58 238.05 44.26 69.64 75.04 26.58 238.05 43.90 61.11 42.69 27.47 203.92 44.31
41.71 45.67 17.01 235.9 31.03 24.83 14.69 17.01 57.65 20.59 49.59 53.18 19.20 235.9 40.19
7.31 0.18 7.07 7.74 7.26 7.14 0.06 7.07 7.20 7.16 7.39 0.17 7.15 7.74 7.36
Moldova All wines (n = 22)
Red wines (n = 8)
White wines (n = 14)
Mean SD Min Max Median Mean SD Min Max Median Mean SD Min Max Median
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Fig. 1. Concentration of elements in the 60 analyzed wine samples.
Soil samples were collected from the corresponding vineyards and therefore from the provenance soil for each wine sample, with the exception of Moldova where, due to multiple locations, different owners of vineyards, and the slow process of receiving the acceptance of sampling soil, no soil samples were taken. From Valea Calugareasca vineyard 15 soil samples were taken from five different locations, at three depths, 0–20 cm (samples code PS1.1, PS2.1, PS3.1, PS4.1, PS5.1), 20–40 cm (samples code PS1.2, PS2.2, PS3.2, PS4.2, PS5.2) and 40–60 cm (samples code PS1.3, PS2.3, PS3.3, PS4.3, PS5.3), while from Murfatlar vineyard only four soil samples were taken, from one location, at depths of 0–20 cm (sample code PSM1), 20–40 cm (sample code PSM2), 40–60 cm (sample code PSM3) and 60–80 cm (sample code PSM4). Soil samples were taken with non-metallic grabs and sealed in plastic bags. 2.3. Sampling procedures Before analysis, the samples were carefully prepared in order to avoid chemical and physical interactions. 2.3.1. Wine sample preparation The wine samples were taken from freshly opened bottles and prepared by a specific organic matter digestion. 2.5 ml of wine were weighed inside Teflon digestion vessels and 2.5 ml of concentrated nitric acid were added. Teflon digestion vessels were previously cleaned in nitric acid solution to avoid crosscontamination. The vessels already capped were placed in a microwave oven followed by the application of the program described in Table 1, optimized in a previous work (Iordache, Geana, Ionete, & Culea, 2010). After cooling to ambient temperature, the reactors were opened and the content was quantitatively transferred into a 50 ml volumetric flask and brought to the volume with ultra pure water. All
the elements were measured from these extraction solutions by ICP-MS. 2.3.2. Soil sample preparation The soil samples were dried, homogenized and then passed through a 20 mesh sieve to obtain very fine particles. The method for microwave digestion using a Mars 5 Microwave System, was optimized in a previous work (Geana, Iordache, Voica, Ionete, & Culea, 2011): 0.25 g of soil, 9 ml of 65% HNO3, 3 mL concentrated HF and 2 ml of concentrated HCl were placed in a clean Teflon digestion vessel. After which, the vessel was closed tightly and placed in the microwave. The digestion was carried out with the program described in Table 2. The vessels were cooled and carefully opened. After the digestion process, each digest was transferred quantitatively with ultra-pure water to a 100-mL volumetric flask. These solutions were then analyzed by ICP-MS. 2.4. ICP-MS analysis The trace elements were measured by using a multi-element analysis after appropriate dilution using an external and standard calibration. The solution was sprayed into flowing argon and passed into a torch which was inductively heated, where the gas was atomized and ionized, forming plasma, which provides a rich source of both excited and ionized atoms. In ICPMS, positive ions in the plasma were focused to a quadrupole mass spectrometer. The method was previously optimized by evaluation of the advantages and limitations of ICP-MS quantitative operational mode for multi-element analysis of wines. Optimum instrumental conditions for ICP-MS measurement are summarized in Table 3. The calibration standards were prepared from the multielemental standard solution, ICP Multi Element Standard Solution XXI CertiPUR, in five concentration ranges 2.5; 5; 10; 25 and 50 lg/l.
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Fig. 2. The contribution of trace – elements concentration across the investigated wine varieties.
2.5. Analytical performance of the method 2.5.1. Wine analysis The limit of detection of the method (LOD) was obtained by analysis of 10 blanks standards mineralized in the same conditions as the samples. The obtained results for LOD were between 0.010 and 0.854 lg/l. The recovery assays for the wine sample of 5 lg/l
concentration, for three replicates of this level of concentration (n = 3) gave the average recovery R (%) between 85.66% and 100.91%. 2.5.2. Soils analysis The conformity of analysis method of soil samples was achieved using SRM NCS ZC 73006 reference material, treated in the same
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Fig. 3. Mean concentration of trace – elements across the wine producing areas.
conditions as soil samples. A good agreement with the recommended values was obtained for all the investigated elements. The recovery R (%) ranged between 80% and 110%. 2.6. Data analysis Concentration of the compounds was evaluated by statistical methods. Descriptive statistical analysis (mean, standard deviation, range and median), correlation analysis and principal component analysis (PCA) were performed using commercial software packages as Microsoft Excel 2010 and Octave 3.4.3. 3. Results and discussions Using the mineral content of the analyzed wines samples as chemical descriptors, statistical methods were applied in order to establish criteria for classification and authentication purposes. The average content of trace-elements in a set of 60 Romanian wines samples are presented in Table 4, the results being expressed in lg/l. The elements Rb and Mn showed the highest mean concentrations (890.11 lg/l for all wines, 1050.46 lg/l for red wines and 783.20 lg/l for white wines samples in the case of Rb and 805.891 lg/l for all wines, 915.90 lg/l for red wines and 732.55 lg/l for white wines samples for Mn), while Ag and Be were the least concentrated. The results indicate that the concentrations measured for all the elements, in white and red wine, are below the limits imposed by Romanian law (Law 244/2002). The variation of Cu content in wine samples is relatively high (Fig. 1), ranging from 25.99 to 2594.79 lg/l. This may originate mainly from Cu accumulation in soil as a consequence of the old practices of using copper sulphate or other copper-based fungicides, to control the vine downy mildew. The frequent use of agrochemicals like pesticides in agricultural practices is a major cause of soil contamination, by trace metal (e.g. copper, nickel, zinc and cadmium) accumulation, which therefore falls ultimately on wine (Medina, 1996). Zn content of studied wines was between 57.44 and 1442.65 lg/l, respectively, with an average of 433.95 lg/l. Ni content ranged between 1.44 and 322.73 lg/l, with an average of 55.15 lg/l, and had the lowest average concentration of 32.14 lg/l for all wines from Moldova region, compared with the other two wine production areas. There are also other elements with high variability, like Mn, but with a framing of concentration values in a well-defined interval per each investigated region. Manganese in small amounts is a natural constituent of grape and wine (Woldemarian & Chadravanshi, 2011); in our study the measured values ranged between 223.5 and 952.5 lg/l for wines
from Muntenia/Valea Calugareasca, 308.37 and 3077.75 lg/l for wines from Dobrogea/Murfatlar, and from 292.73 to 1403.58 lg/l for wines from the Moldova region, respectively. Compared with the other two regions, the wines from Muntenia/Valea Calugareasca have slightly lower amounts of manganese. Strontium was higher in wines from Valea Calugareasca Vineyard, with values ranging from 278.67 to 1600.68 lg/l. 3.1. The content of metals in the wine varieties The comparison of trace-elements concentrations performed by grouping the wines in grape varieties (Fig. 2), reveals a great variation for most of the metals. An overlapping of concentration ranges for the elements analyzed is observed, which make the differentiation on wine varieties inconclusive. Rb and Sr appears to have higher concentration levels in red wines (Rb average 1050.46 lg/l; values between 743.4 and 2066.21 lg/l for 70% of samples; Sr from 410.05 to 1.538.44 lg/l for 70% of samples and an average of 675.06 lg/l for all samples) than in white wines (Rb average783.20 lg/l; values between 219.56 and 884.9 lg/l for 70% of samples; Sr from 144.34 to 489.76 lg/l for 70% of samples and an average of 450.87 lg/l for all samples). Opposite trend, higher concentration values in white wines than in red ones, occurs for Cu and Co. The average value retrieved for cooper was 602.41 lg/l in white wines towards 347.8 lg/l in red wines. For Co, the mean values were 5.63 lg/l for white wines and 2.43 lg/l for red ones, the highest and lowest concentration being encountered in specific Romanian varieties, Columna (42.06 lg/l) and Mamaia (0.68 lg/l), respectively. However, having an overall view to data in Table 2, we do not see a consistent pattern for the elements and it does not seem that on average red wines have higher trace element concentrations than white ones. The opinion that concentration of elements should be significantly higher in red than in white wines due to longer skin contact in the vinification process of red wines is not supported by our data. 3.2. The content of metals in the wine production areas By comparing the mean values across wine producing areas, the concentration ranges of most elements were overlapped (Fig. 3). Nevertheless elements less influenced by the environmental or technological factors such as Mn, Rb, Sr and V exhibit a distinctive concentration pattern across wine regions. The higher mean concentration of Mn and Rb, for both red and white wines, was measured in wines produced in the south-eastern part of Romania, at Dobrogea/Murfatlar region and the lower mean contents in wines from Muntenia/Valea Calugareasca. For the red and
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Fig. 4. Correlation of Mn element with Cr, Sr, Ag and Co to differentiate the origin of wine samples.
white wines, the highest average content of Sr was in the wines from Muntenia/Valea Calugareasca region and the lowest in wines from Moldova, while for V the values were higher for wines produced in Moldova and lower in wines from Muntenia/Valea Calugareasca. Analyzing the results by performing a number of 65 combinations of the measured elements, few possibilities for discrimination by origin were emphasized (Fig. 4). The correlation of Mn with Cr, Sr, Rb, Ag or Co reveals a very good differentiation of wines from
Dobrogea/Murfatlar and Muntenia/Valea Calugareasca regions, while the wines from Moldova show a partial overlap with wines from the other two areas. The correlation of Mn with Cr, Sr, Rb, Ag or Co content in wines (Fig. 4) shows a reasonable degree of discrimination with wine geographical origin. The elemental analysis shows that the geographical origin represents an influencing factor of the wines’ chemical composition, an identification criterion of Romanian wines.
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Fig. 5. Score plot of the three principal components of trace-elements in the Romanian wine samples (n = 60).
Table 5 Experimental results of the elements determined (mg/kg) in soil samples. Sample code
Cr
Mn
V
Co
Ni
Cu
Zn
Valea Calugareasca PS 1.1 PS 1.2 PS 1.3 PS 2.1 PS 2.2 PS 2.3 PS 3.1 PS 3.2 PS 3.3 PS 4.1 PS 4.2 PS 4.3 PS 5.1 PS 5.2 PS 5.3
39.72 40.36 38.68 36.18 34.61 34.63 32.75 35.68 31.65 34.92 39.92 32.72 30.70 34.88 32.07
393.55 381.93 329.73 413.41 331.30 360.33 357.26 311.32 325.19 298.16 282.47 305.71 302.94 215.97 226.37
32.61 32.40 29.36 25.66 24.04 21.82 19.29 18.85 16.62 14.60 14.48 13.61 12.05 11.66 10.26
4.36 3.89 3.40 3.44 2.86 2.58 2.41 2.04 1.92 2.08 1.75 1.52 1.42 1.12 0.94
10.96 11.29 10.38 8.52 7.29 6.38 5.40 5.34 4.74 4.33 3.89 3.38 2.97 2.59 2.12
37.13 12.41 10.23 13.62 8.75 8.89 16.27 5.60 5.88 7.60 5.75 4.94 13.28 4.10 3.07
22.44 24.05 17.82 16.71 14.29 12.98 14.16 11.32 10.68 11.09 9.23 8.51 8.40 6.28 5.37
2.76 2.81 2.46 2.33 2.14 1.86 2.00 1.43 1.22 0.90 0.96 0.92 1.11 0.59 0.44
8.09 4.62 5.62 4.96 4.88 3.35 3.80 3.51 2.58 2.29 3.12 1.67 1.89 1.95 0.91
Murfatlar PS M1 PS M2 PS M3 PS M4
32.24 27.03 26.71 32.10
299.10 282.62 257.20 240.33
7.06 6.41 6.19 5.12
0.74 0.74 0.77 0.49
1.67 1.58 1.48 0.97
5.87 4.18 3.65 2.32
6.72 7.69 7.70 2.98
83.88 0.19 0.09 0.07
2.76 2.20 2.75 2.68
Although metal ions compositional information on Romanian wines is scarce, our results are in agreement with the values reported in literature by other authors (Voica et al., 2009).
As
Rb
Sr
Ag
U
11.28 11.83 8.11 12.18 8.40 10.30 8.53 7.07 7.72 5.21 2.66 4.65 2.73 2.20 2.79
1.13 1.03 1.05 0.93 1.03 0.99 0.92 0.89 0.84 0.66 0.68 0.67 0.64 0.61 0.60
0.84 0.56 0.54 0.49 0.55 0.41 0.41 0.36 0.26 0.15 0.16 0.06 0.08 0.02 0.10
462.31 538.63 511.39 370.05
1.07 0.67 0.69 0.62
0.18 0.02 0.02 0.03
3.3. Principal component analysis The data resulting from the analysis consisted of 12 variables for each of the 60 wine samples. These variables represent the me-
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those of Murfatlar vineyard, while in the soil samples the level of Sr was significantly higher in the Murfatlar region. The concentrations of studied elements decreased with soil sampling depth which means that the surface soil area can be influenced by the environmental contaminants. According to the Romanian legislation (Order 756/1997), the metal concentration levels in studied soil samples show normal values for sensitive soil. 4. Conclusion
Fig. 6. Graphical representation of elements average concentration in soil samples.
tal content profile of each of the wines (Cr, Ni, Rb, Sr, Ag, Zn, Mn, Cu, Co, V, Pb, and Be). We needed to find a correlation between the geographical region and the 12 variables. For this purpose a principal component analysis (PCA) was applied in order to reduce the dimensionality of the data while retaining as much of the variance as possible. The number of principal components is less than or equal to the number of original variables. The PCA was performed using Octave 3.4.3 free software. We chose to represent the first 3 principal components carrying as much as 83% of the variance of the original data. The first principal component PC1 carries 42% of the data variance, the second principal component PC2 carries 27% and the third principal component PC3 14% of the original data variance (Fig. 5). Reducing the data dimensionality from 12 to just 3 while preserving 83% of the variance allows us to represent it in a 3 dimensional space, which is easier to visualize and reveals some clustering with respect to the geographical regions. After plotting the wine samples in the 3 dimensional space, we engulfed all wines from a specific geographic region in a convex envelope in order to better visualize the clustering of the data. The wines from Valea Calugareasca and Murfatlar geographical regions are more easily discriminated while it is more difficult to separate the wines from Moldova.
3.4. The correlation of trace-elements from wines and their provenance soil Additionally, the mineral characterization of soils from Valea Calugareasca and Murfatlar vineyards according to Cr, Mn, V, Co, Ni, Cu, Zn, As, Rb, Sr, Ag, and U content was performed. The obtained results are described in Table 5. The elements Mn and Sr showed the highest level of concentration in the studied soil samples, ranging from 215.97 to 413.41 mg/ kg for Mn and from 2.20 to 538.63 mg/kg for Sr, respectively. The levels of the studied elements were greater from soils of Valea Calugareasca vineyard for almost all the elements with exception of As and Sr (Fig. 6). The values for Sr were significantly higher in soils from the Murfatlar region. We expected to have a correlation between the level of the elements in wine samples and the corresponding soil samples. This hypothesis was applied for the elements Ni, Ag, Be, Cr, Zn, Pb, Co and Cu. We noticed a discrepancy in the case of the highest level of Sr in wine samples from Valea Calugareasca compared with
The elements Mn, Cr, Sr, Ag and Co were identified as indicators for the discrimination between wines and soils of the three major wine-producing regions in Romania. In the case of the wines and soils studied a correlation was observed between the elemental composition of the wine and its provenance soil. This premise is important for the application of the fingerprinting methodology based on multi-element data and statistical analysis for the classification of Romanian wines according to geographical origin. The results of this study constitute the starting point in building a database for Romanian wines. This methodology can be applied for classification of unknown wine according to geographical origin. The methodology will be applied to the other Romanian wine regions for intraregional and inter-regional classification of wines, to extend the elemental characterization of the wine and soil. This classification will help to prevent fraudulent practices in the wine industry. Acknowledgements The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. References Almeida, C. M. R., & Vasconcelos, M. T. S. D. (2003). Multielement composition of wines and their precursors including provenance soil and their potentialities as fingerprints of wine origin. Journal of Agricultural Food Chemistry, 51(16), 4788– 4798. http://dx.doi.org/10.1016/j.foodchem.2008.05.043. Álvarez, M., Moreno, I. M., Jos, Á., Cameán, A. M., & González, A. G. (2007). Differentiation of two Andalusian DO ‘fino’ wines according to their metal content from ICP-OES by using supervised pattern recognition methods. Microchemical Journal, 87, 72–76. http://dx.doi.org/10.1016/j.microc.2007. 05.007. Arvanitoyannis, I. S., Katsota, M. N., Psarra, E. P., Soufleros, E. H., & Kallithraka, K. (1999). Application of quality control methods for assessing wine authenticity. Trends Food Science & Technology, 10, 321–336. http://dx.doi.org/10.1016/ S0924-2244(99)00053-9. Coetzee, P. P., Steffens, F. E., Eiselen, R. J., Augustyn, O. P., Balcaen, L., & Vanhaecke, F. (2005). Multi-element analysis of South African wines by ICP-MS and their classification according to geographical origin. Journal of Agricultural Food Chemistry, 52(13), 5060–5066. http://dx.doi.org/10.1021/jf048268n. Geana, I., Iordache, A., Voica, C., Ionete, R., & Culea, M. (2011). Comparison of three digestion methods for heavy metals determination in soils and sediments materials by ICP-MS technique. Asian Journal of Chemistry, 23(12), 52123–55216. Giaccio, M., & Vicentini, A. (2008). Determination of the geographical origin of wines by means of the mineral content and the stable isotope ratios: a review. Journal of Commodity Science, Technology and Quality, 47(I–IV), 267–284. http:// www.sci.unich.it/~jcs/2008/2008-02-14.pdf. Gonzálvez, A., Llorens, A., Cervera, M. L., Armenta, S., & De La Guardia, M. (2009). Elemental fingerprint of wines from the protected designation of origin Valencia. Food Chemistry, 112, 26–34. http://dx.doi.org/10.1016/j.foodchem. 2008.05.043. Gremaud, G., Quaile, S., Piantini, U., Pfammatter, E., & Corvi, C. (2004). Characterization of Swiss vineyards using isotopic data in combination with trace elements and classical parameters. European Food Research and Technology, 219, 97–104. http://dx.doi.org/10.1007/s00217-004-0919-0. Iordache, A., Geana, I., Ionete, R., & Culea, M. (2010). The optimization of the method for metals content determination in Romanian wines by ICP-MS after microwave mineralization. Progress of Cryogenics and Isotopes Separation, 13(1), 137–145. Jos, Á., Moreno, I., González, A. G., Repetto, G., & Cameán, A. M. (2004). Differentiation of sparkling wines (cava and champagne) according to their
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mineral content. Talanta, 63, 377–382. http://dx.doi.org/10.1016/j.talanta.2003. 11.015. Korenovska, M., & Suhaj, M. (2005). Identification of some Slovakian and European wines origin by the use of factor analysis of elemental data. European Food Research and Technology, 221, 550–558. http://dx.doi.org/10.1007/s00217-0051193-5. La Pera, L., Gudo, G., Rando, R., Di Bella, G., Maisano, R., & Salvo, F. (2008). Statistical study of the influence of fungicide treatments (mancozeb, zoxamide and copper oxychloride) on heavy metal concentrations in Sicilian red wine. Food Additives and Contaminants Part A, 25, 302–313. http://dx.doi.org/10.1080/ 02652030701329603. Medina, B. (1996). Food Authentication, Chapter 3 Wine authenticity. In: P. R. Ashurst & H. J. Dennis (Eds.), (pp. 60–106). London: Blackie Academic & Professional, ISBN 07514 03415. Mihucz, V. G., Done, C. J., Tatar, E., Virag, I., Zaray, G., & Baiulescu, E. G. (2006). Influence of different bentonites on the rare earth element concentrations of clarified Romanian wines. Talanta, 70, 984–990. http://dx.doi.org/10.1016/ j.talanta.2006.05.080. Sass-Kiss, A., Kiss, J., Havadi, B., & Adanyi, N. (2008). Multivariate statistical analysis of botrytised wines of different origin. Food Chemistry, 110, 742–750. http:// dx.doi.org/10.1016/j.foodchem.2008.02.059.
Schlesier, K., Fauhl-Hassek, C., Forina, M., Cotea, V., Kocsi, E., Schoula, R., et al. (2009). Characterization and determination of the geographical origin of wines. Part I: Overview. European Food Research and Technology, 230, 1–13. http:// dx.doi.org/10.1007/s00217-009-1140-y. Suhaj, M., & Korenovska, M. (2005). Application of elemental analysis for identification of wine origin. A review. Acta Alimentaria, 34(4), 393–401. http://dx.doi.org/10.1556/AAlim.34.2005.4.7. Van Der Linde, G., Fischer, J. L., & Coetzee, P. P. (2010). Multi-element analysis of South African wines and their provenance soils by ICP-MS and their classification according to geographical origin using multivariate statistics. European Food Research and Technology, 31(2), 143–153. http://www.sasev.org/ journal-sajev/sajev-articles/volume-31-2/van%20der%20Linde%20et%20al%20pp%20143%20to%20153.pdf. Voica, C., Dehelean, A., & Pamula, A. (2009). Method validation for determination of heavy metals in wine and slightly alcoholic beverages by ICP-MS. Journal of Physics: Conference Series 182 012036, http://iopscience.iop.org/1742-6596/ 182/1/012036. Woldemarian, D. M., & Chadravanshi, B. S. (2011). Concentration levels of essential and non-essential elements in selected Ethiopian wines. Bulletin of the Chemical Society of Ethiopia, 25(2), 169–180. http://www.ajol.info/index.php/bcse/article/ viewFile/65852/53571.
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Geographical origin identification of Romanian wines by ICP-MS elemental analysis Irina Geana a, Andreea Iordache a, Roxana Ionete a, Adrian Marinescu a, Aurora Ranca b, Monica Culea c,⇑ a
National R&D Institute for Cryogenics and Isotopic Technologies’ – ICIT Rm. Valcea, 4 Uzinei St, 240050 Rm. Valcea, Romania Research Station for Viticulture and Oenology Murfatlar, Murfatlar 905100, Romania c Babes-Bolyai’ University, 1 M. Kogalniceanu St, Cluj-Napoca 400084, Romania b
a r t i c l e
i n f o
Article history: Received 4 July 2012 Received in revised form 9 October 2012 Accepted 21 November 2012 Available online 5 December 2012 Keywords: Trace elements Wine Authentication Acid extraction ICP-MS Geographical origin
a b s t r a c t Trace elemental analysis, besides its ability to determine stable isotopes ratios, represents a possible complementary tool useful to differentiate wines based on their regional origins. Wines and their provenance soils from two major wine producing areas in Southeast Romania (‘Valea Calugareasca’ and ‘Murfatlar’), and also wine from the region of Moldova (Eastern Romania) were analyzed by inductively coupled plasma mass spectrometry (ICP-MS), and statistical data of elemental composition was used to differentiate these wines according to grape type and geographical origin. Moreover, this study gathers relevant elemental trace composition of wines produced in most important Romanian vineyards, thus offering a useful wine differentiation tool by their production district. The results show that the differentiation of Romanian wines according to their provenance is based on the following main elements: Ni, Ag, Cr, Sr, Zn, and Cu for Valea Calugareasca, Rb, Zn, and Mn for Murfatlar, and Pb, Co, and V for Moldova. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Geographical origin and authenticity are both factors influencing the overall perception of wines in terms of quality, and price, hence, being of great importance to consumers and commercial wine producers. Multi elemental trace analysis and rare earth element analysis by spectroscopic methods, such as AAS or ICP-MS, are valuable tools for identifying the origin of a wine (Almeida & Vasconcelos, 2003; Coetzee et al., 2005). Given the fact that the natural diffusional movement of elemental traces follows a pattern, moving from rocks to soil, and from the soil to the grape, allows for wines to be differentiated through the elemental analysis of their provenance soils. Besides the ‘natural’ mineral content of the soil and the grape vine’s capacity to uptake and accumulate these elements in the grape berry, there are several other variables such as environmental contamination, agricultural practices, or wine-making treatments with metal containing products (e.g. bentonite addition in the wine clarifying step) altering the final composition, and concentration of minerals in wines. These factors may markedly affect the elemental composition of wines, and render the correlation between the wine and its provenance soil to be of a more complex nature (La Pera et al., 2008; Mihucz et al., 2006; Suhaj & Korenovska, 2005).
⇑ Corresponding author. Tel.: +40 264593833x5712; fax: +40 264590818. E-mail addresses: [email protected] (I. Geana), [email protected] (A. Iordache), [email protected] (R. Ionete), [email protected] (A. Marinescu), [email protected] (A. Ranca), [email protected] (M. Culea). 0308-8146/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodchem.2012.11.104
The identification of major and trace elements in wines from different regions of the world with the aim to survey the content of certain metals, and test the wine provenance, or region of origin, has been previously studied in countries like Spain (Álvarez, Moreno, Jos, Cameán, & González, 2007; Gonzálvez, Llorens, Cervera, Armenta, & De La Guardia, 2009; Jos, Moreno, González, Repetto, & Cameán, 2004), Czech Republic, Slovakia (Korenovska & Suhaj, 2005), Hungary (Sass-Kiss, Kiss, Havadi, & Adanyi, 2008), Switzerland (Gremaud, Quaile, Piantini, Pfammatter, & Corvi, 2004), South Africa (Van Der Linde, Fischer, & Coetzee, 2010), Germany, Italy, Portugal, and France (Giaccio & Vicentini, 2008). Wines typically contain macro-elements such as Na, K, Mg, Ca (c > 10 mg/l), micro-elements such as Fe, Cu, Zn, Mn, Pb (c > 10 lg/ l), and ultra microelements such as Cr, As, Cd, and Ni (c < 10 lg/l) (Voica, Dehelean, & Pamula, 2009). Certain macro and micronutrients, such as Na, K, Ca, Fe, Cu, and Zn, show significant changes during the technological processes involved in wine making and are a reason for the attention given to the elements which show very small variation, although they are found in small quantities or only in trace amounts (e.g. Cr, Co, Sb, Cs, Sc, Eu, Hf, Tl, etc.). Trace elements less affected by the wine making processes are the alkaline earth metals, of which Li and Rb are the most relevant for geographical origin identification. According to earlier works, the most frequently quantified and cited elements used for wine authentication are K, Na, Fe, Y, Rb, Ca, Cu, Cr, Co, Sb, Cs, Br, As, Ag, Li, Ba, Sr, Mg, Al and Mn (Arvanitoyannis, Katsota, Psarra, Soufleros, & Kallithraka, 1999; Medina, 1996). For Romania, according to studies performed to date, the main elements allowing a differentiation between wines are Mn and Sr (Suhaj & Korenovska, 2005).
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Table 1 The program of the microwave furnace Mars 5 Microwave System for wine extractions. Step
1 2
Power Level
%
600 15 min-cooling
100
Ramp time (min)
Pressure (psi)
Temperature (°C)
Hold time (min)
10
800
180
20
Ramp time (min)
Pressure (psi)
Temperature (°C)
Hold time (min)
6
800
200
20
Table 2 Digestion program for soil extractions. Step
1 2
Power Level
%
800 15 min-cooling
100
Since metal ions play significant roles in the wine making process (e.g. oxidation reactions, metabolism of wine yeast and bacteria) and, to our knowledge, metal ions compositional information used to authenticate Romanian wines is scarce, we focused our attention on three of the main wine regions covering some of the famous Romanian viticultural areas, although Romania is divided into 8 wine regions, and 37 areas of production (33 red wines, and 67 white wines) (Schlesier et al., 2009). The aim of the present paper is to investigate the metal content profile (considering the elements Cr, Ni, Rb, Sr, Ag, Zn, Mn, Cu, Co, V, Pb, and Be) of authentic wine samples from several famous Romanian vineyards of Muntenia (Valea Calugareasca vineyard), Dobrogea (Murfatlar) and Moldova (Iasi, Cotnari, Bujoru, Panciu, Odobesti, Nicoresti) in an attempt to differentiate wines by region depending on their elemental composition. A critical factor for choosing the appropriate analytical method for the elemental fingerprinting of wines was the multi-element detection capability, with ICP-MS being a suitable technique for accurate and fast determination of trace and ultra-trace elements in the same sample. We further statistically analyzed the distribution of elements in the soils of studied regions, and the correlation between the elemental composition of analyzed wines and their provenance soil was substantiated. 2. Materials and methods 2.1. Apparatus and reagents Analytical measurements were performed using an inductively coupled plasma mass spectrometer (ICP-MS Varian 820-MS from Varian, Australia) equipped with an SPS-3 autosampler (Varian, Australia), a micro-concentric nebulizer, nickel cones, and a peristaltic sample delivery pump, running a quantitative analysis mode. Each sample was analyzed in duplicate, and each analysis consisted of five replicates. The gaseous argon used to form the plasma in the ICP-MS was of purity 6.0 (Messer, Austria).
A Mars 5 Microwave System (CEM Microwave Technology Ltd, UK) equipped with a microwave acid digestion bomb made from Teflon, with a capacity of 100 ml was used for microwave digestion. High purity ICP Multi Element Standard Solution XXI CertiPUR obtained from Merck (Darmstadt, Germany) was used for the calibration curve in the quantitative analysis. The ICP Multi Element Standard Solution XXI CertiPUR was a mixture of 10.0 mg/l of As, Be, Bi, Co, Cr, Cu, K, Li, In, Tl, Se, Rb and V, 10.1 mg/l of U, Mg, Ni, and Ba and 9.9 mg/l of Al, Cd, Fe, Ag, Ni and Zn. HNO3 69% (w/v), concentrated HF and HCl, reagent grade from Merck and ultrapure water with a maximum resistivity of 18.2 M X cm 1 , obtained from a Milli-Q Millipore system (Bedford, MA, USA) were used for sample treatment and sample dilution. 2.2. Wine and soil samples For this study, authentic wine samples from different vineyards located in some of the most important Romanian wine regions, namely the Dobrogea region (Murfatlar vineyard), Muntenia region (Valea Calugareasca vineyard) and Moldova region (Iasi, Cotnari, Panciu, Odobesti, Nicoresßti and Bujoru vineyards) were investigated. The wine samples were obtained by microvinification based on EC Regulation No. 2729/2000, consolidated with EC Regulation No. 2030/2006. A set of 60 wine samples were collected from the three most important Romanian wine regions. Of these, 26 were red wines. 20 samples were from Valea Calugareasca vineyard, 18 samples from Murfatlar vineyard and 22 from the Moldova Region. Most samples collected (36) were from the harvest of 2010, while the remaining 24 samples were from the harvest of 2009 (samples for checking the stability of measured parameters), and all samples were of high quality. The samples represent eighteen grape varieties, from native (Feteasca Regala – FR, Feteasca Alba – FA, Mamaia – MM, Columna – CO, Tamaioasa Romaneasca – TR, Grasa de Cotnari – GR, Babeasca Neagra – BN, Feteasca Neagra – FN,
Table 3 Instrumental (a) and data acquisition (b) parameters of ICP-MS. (a) Instrumental parameters RF power Argon gas flow Nebulizer Plasma Lens voltage Mirror lens left Mirror lens right Mirror lens bottom Sample uptake rate
(b) Data acquisition parameters for quantitative mode 1.4 kW 1. L/min 18.0 L/min 37 V 31 V 30 V 40 s
Measuring mode Point per peak Scans/Replicate Replicate/Sample Dwell time (ms)
Peak hopping 3 10 10 1
Integration time (s)
395.08 s
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Francusa – FC), to foreign origin (Saugvinon – S, Pinot Gris – PG, Chardonnay – CH, Riesling Italian – RI, Cabernet Saugvinon – CS, Pinot Noir – PN, Merlot – ME, Burgund Mare – BM, Muscat Ottonel – MO), both red and whites, were analyzed. Their ethanol contents ranged from 11% to 14.8% v/v.
The plastic containers used for storing and treating the samples were cleaned to avoid contamination of samples with traces of any metal. Containers were treated with nitric acid and then washed with ultra pure water. Once opened, wine samples were treated according to the preparation procedure.
Table 4 Experimental results of elements (lg/L) measured in wine samples and their geographical origin. Elements
Cr
Ni
Rb
Sr
Ag
Zn
Mn
Cu
Co
V
Pb
Be
All three investigated regions All wines (n = 60) Mean SD Min Max Median Red wines (n = 26) Mean SD Min Max Median White wines (n = 34) Mean SD Min Max Median
254.79 262.31 60.96 1725.80 181.64 233.00 176.96 109.58 982.91 195.68 269.32 308.00 60.96 1725.80 174.29
55.15 61.26 1.44 322.73 35.13 51.67 52.05 5.07 217.09 32.54 57.48 67.31 1.44 322.73 37.35
890.11 458.92 219.56 2066.21 781.45 1050.46 496.11 410.05 2066.21 843.01 783.20 404.84 219.56 1755.39 721.15
540.55 291.73 144.34 1600.68 471.28 675.06 310.23 353.02 1538.44 599.63 450.87 244.01 144.34 1600.68 408.37
7.01 2.92 0.55 12.16 7.44 7.21 3.39 0.86 12.16 8.21 6.87 2.60 0.55 11.26 7.22
433.95 265.16 57.44 1442.65 385.48 376.02 215.46 57.44 1004.79 317.89 472.57 290.14 178.50 1442.65 406.57
805.89 533.09 223.5 3077.75 604.79 915.90 571.01 280.05 3077.75 857.46 732.55 500.96 223.5 2454.49 562.60
500.57 466.48 25.99 2594.79 367.59 347.81 291.35 25.99 1216.71 250.13 602.41 533.26 82.06 2594.79 522.46
4.35 5.83 0.34 42.06 2.76 2.43 1.40 0.34 6.03 2.06 5.63 7.19 1.31 42.06 3.35
47.61 35.73 18.61 238.05 41.13 46.30 42.93 18.61 238.05 32.31 48.48 30.65 22.25 203.92 41.45
37.97 28.79 17.01 235.9 33.04 30.68 11.47 17.01 57.65 27.71 42.83 35.35 19.20 235.9 37.54
7.29 0.27 6.7 8.05 7.20 7.18 0.23 6.7 7.78 7.16 7.37 0.28 7.04 8.05 7.27
Muntenia region/Valea Calugareasca vineyard All wines (n = 20) Mean 299.69 SD 262.43 Min 113.13 Max 1101.09 Median 208.98 Red wines (n = 10) Mean 289.65 SD 250.62 Min 113.13 Max 982.91 Median 222.38 White wines (n = 10) Mean 309.73 SD 286.97 Min 134.73 Max 1101.09 Median 208.98
70.77 67.55 14.4 322.73 53.38 59.76 40.19 14.4 137.51 48.82 81.78 88.02 23.65 322.73 54.40
718.40 295.38 219.56 1440.54 682.47 886.12 304.75 560.54 1440.54 773.56 550.68 169.75 219.56 792.94 545.59
709.02 386.29 278.67 1600.68 570.69 823.23 387.36 475.22 1538.44 619.28 594.80 368.76 278.67 1600.68 514.03
8.86 1.48 6.78 12.16 8.43 9.22 1.66 7.47 12.16 8.49 8.50 1.26 6.78 11.20 8.37
442.53 239.08 248.22 1356.94 414.33 360.40 109.59 248.22 580.82 314.46 524.65 306.06 268.16 1356.94 453.44
437.68 160.01 223.5 952.5 447.94 501.31 183.76 280.05 952.50 466.67 374.06 106.25 223.5 506.55 367.71
703.38 528.36 148.9 2594.79 658.88 520.44 360.42 148.90 1216.71 401.76 886.31 620.55 450.41 2594.79 761.53
3.45 2.26 1.62 11.58 3.03 2.97 1.57 1.62 6.03 2.21 3.93 2.79 1.63 11.58 3.35
28.73 5.17 18.61 37.66 27.71 27.15 3.94 18.61 33.07 27.69 30.30 5.94 22.25 37.66 28.48
38.19 14.05 20.64 80.53 36.51 31.71 10.58 20.64 50.27 30.25 44.68 14.54 31.16 80.53 39.25
7.15 0.07 7.04 7.31 7.16 7.18 0.07 7.08 7.31 7.16 7.13 0.07 7.04 7.22 7.13
Dobrogea region/Murfatlar vineyard All wines (n = 18) Mean SD Min Max Median Red wines (n = 8) Mean SD Min Max Median White wines (n = 10) Mean SD Min Max Median
250.21 121.90 120.51 533.03 254.26 248.62 88.54 121.48 339.9 282.20 251.22 143.36 120.51 533.03 171.55
65.93 72.39 9.58 263.77 34.59 78.05 72.72 19.74 217.09 60.24 58.22 74.62 9.58 263.77 31.59
1125.92 592.46 346.32 2066.21 1116.07 1215.49 739.42 410.05 2066.21 1163.02 1068.92 510.08 346.32 1755.39 1072.16
504.80 207.00 225.32 808.61 449.48 598.53 192.95 360.66 796.42 613.75 445.16 201.11 225.32 808.61 434.05
3.64 2.83 0.55 7.01 1.91 2.83 2.62 0.86 6.64 1.30 4.16 2.96 0.55 7.01 6.58
476.05 338.07 181.53 1442.65 360.17 456.94 207.52 214.35 845.17 459.42 488.20 409.92 181.53 1442.65 336.09
1331.94 646.62 308.37 3077.75 1206.76 1468.67 711.64 1110.70 3077.75 1214.47 1244.92 620.91 308.37 2454.49 1173.05
240.41 183.29 25.99 724.71 201.15 163.23 129.40 25.99 367.02 103.84 289.52 200.60 82.06 724.71 270.77
4.56 9.55 0.34 42.06 1.85 2.10 1.56 0.34 5.04 2.11 6.21 12.11 1.31 42.06 1.64
48.77 11.49 27.33 65.56 54.16 50.33 12.01 27.33 65.56 53.79 47.78 11.62 30.35 58.95 54.52
33.15 5.97 24.74 44.42 31.81 35.07 7.50 25.14 44.42 34.70 31.93 4.76 24.74 41.92 31.05
7.43 0.41 6.7 8.05 7.47 7.23 0.42 6.7 7.78 7.20 7.56 0.36 7.04 8.05 7.62
217.72 339.99 60.96 1725.80 133.22 136.44 38.31 109.58 219.86 122.37 255.66 409.78 60.96 1725.80 147.78
32.14 35.86 1.44 127.05 18.97 13.72 8.70 5.07 27.21 11.01 40.73 40.59 1.44 127.05 28.88
853.26 389.16 262.49 1715.68 847.31 1120.20 419.67 743.40 1715.68 866.29 728.69 315.33 262.49 1351.14 773.42
416.64 158.73 144.34 855.37 368.04 539.93 204.67 353.02 855.37 473.89 359.10 93.23 144.34 529.85 334.49
8.07 1.15 6.65 10.47 7.64 8.73 1.11 7.41 10.47 8.53 7.76 1.06 6.65 10.18 7.38
391.72 223.27 57.44 1004.79 383.09 317.42 323.76 57.44 1004.79 305.95 426.40 160.63 178.50 940.98 400.81
710.23 246.76 292.73 1403.58 643.96 955.39 219.15 768.14 1403.58 862.80 595.81 163.32 292.73 904.98 595.63
529.06 483.28 176.00 2515.37 417.09 285.77 141.02 176.00 512.52 204.43 642.60 546.26 285.00 2515.37 537.04
4.99 3.96 1.20 17.39 3.70 1.98 0.75 1.20 3.41 1.99 6.40 4.08 2.08 17.39 6.10
63.82 53.30 26.58 238.05 44.26 69.64 75.04 26.58 238.05 43.90 61.11 42.69 27.47 203.92 44.31
41.71 45.67 17.01 235.9 31.03 24.83 14.69 17.01 57.65 20.59 49.59 53.18 19.20 235.9 40.19
7.31 0.18 7.07 7.74 7.26 7.14 0.06 7.07 7.20 7.16 7.39 0.17 7.15 7.74 7.36
Moldova All wines (n = 22)
Red wines (n = 8)
White wines (n = 14)
Mean SD Min Max Median Mean SD Min Max Median Mean SD Min Max Median
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Fig. 1. Concentration of elements in the 60 analyzed wine samples.
Soil samples were collected from the corresponding vineyards and therefore from the provenance soil for each wine sample, with the exception of Moldova where, due to multiple locations, different owners of vineyards, and the slow process of receiving the acceptance of sampling soil, no soil samples were taken. From Valea Calugareasca vineyard 15 soil samples were taken from five different locations, at three depths, 0–20 cm (samples code PS1.1, PS2.1, PS3.1, PS4.1, PS5.1), 20–40 cm (samples code PS1.2, PS2.2, PS3.2, PS4.2, PS5.2) and 40–60 cm (samples code PS1.3, PS2.3, PS3.3, PS4.3, PS5.3), while from Murfatlar vineyard only four soil samples were taken, from one location, at depths of 0–20 cm (sample code PSM1), 20–40 cm (sample code PSM2), 40–60 cm (sample code PSM3) and 60–80 cm (sample code PSM4). Soil samples were taken with non-metallic grabs and sealed in plastic bags. 2.3. Sampling procedures Before analysis, the samples were carefully prepared in order to avoid chemical and physical interactions. 2.3.1. Wine sample preparation The wine samples were taken from freshly opened bottles and prepared by a specific organic matter digestion. 2.5 ml of wine were weighed inside Teflon digestion vessels and 2.5 ml of concentrated nitric acid were added. Teflon digestion vessels were previously cleaned in nitric acid solution to avoid crosscontamination. The vessels already capped were placed in a microwave oven followed by the application of the program described in Table 1, optimized in a previous work (Iordache, Geana, Ionete, & Culea, 2010). After cooling to ambient temperature, the reactors were opened and the content was quantitatively transferred into a 50 ml volumetric flask and brought to the volume with ultra pure water. All
the elements were measured from these extraction solutions by ICP-MS. 2.3.2. Soil sample preparation The soil samples were dried, homogenized and then passed through a 20 mesh sieve to obtain very fine particles. The method for microwave digestion using a Mars 5 Microwave System, was optimized in a previous work (Geana, Iordache, Voica, Ionete, & Culea, 2011): 0.25 g of soil, 9 ml of 65% HNO3, 3 mL concentrated HF and 2 ml of concentrated HCl were placed in a clean Teflon digestion vessel. After which, the vessel was closed tightly and placed in the microwave. The digestion was carried out with the program described in Table 2. The vessels were cooled and carefully opened. After the digestion process, each digest was transferred quantitatively with ultra-pure water to a 100-mL volumetric flask. These solutions were then analyzed by ICP-MS. 2.4. ICP-MS analysis The trace elements were measured by using a multi-element analysis after appropriate dilution using an external and standard calibration. The solution was sprayed into flowing argon and passed into a torch which was inductively heated, where the gas was atomized and ionized, forming plasma, which provides a rich source of both excited and ionized atoms. In ICPMS, positive ions in the plasma were focused to a quadrupole mass spectrometer. The method was previously optimized by evaluation of the advantages and limitations of ICP-MS quantitative operational mode for multi-element analysis of wines. Optimum instrumental conditions for ICP-MS measurement are summarized in Table 3. The calibration standards were prepared from the multielemental standard solution, ICP Multi Element Standard Solution XXI CertiPUR, in five concentration ranges 2.5; 5; 10; 25 and 50 lg/l.
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Fig. 2. The contribution of trace – elements concentration across the investigated wine varieties.
2.5. Analytical performance of the method 2.5.1. Wine analysis The limit of detection of the method (LOD) was obtained by analysis of 10 blanks standards mineralized in the same conditions as the samples. The obtained results for LOD were between 0.010 and 0.854 lg/l. The recovery assays for the wine sample of 5 lg/l
concentration, for three replicates of this level of concentration (n = 3) gave the average recovery R (%) between 85.66% and 100.91%. 2.5.2. Soils analysis The conformity of analysis method of soil samples was achieved using SRM NCS ZC 73006 reference material, treated in the same
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Fig. 3. Mean concentration of trace – elements across the wine producing areas.
conditions as soil samples. A good agreement with the recommended values was obtained for all the investigated elements. The recovery R (%) ranged between 80% and 110%. 2.6. Data analysis Concentration of the compounds was evaluated by statistical methods. Descriptive statistical analysis (mean, standard deviation, range and median), correlation analysis and principal component analysis (PCA) were performed using commercial software packages as Microsoft Excel 2010 and Octave 3.4.3. 3. Results and discussions Using the mineral content of the analyzed wines samples as chemical descriptors, statistical methods were applied in order to establish criteria for classification and authentication purposes. The average content of trace-elements in a set of 60 Romanian wines samples are presented in Table 4, the results being expressed in lg/l. The elements Rb and Mn showed the highest mean concentrations (890.11 lg/l for all wines, 1050.46 lg/l for red wines and 783.20 lg/l for white wines samples in the case of Rb and 805.891 lg/l for all wines, 915.90 lg/l for red wines and 732.55 lg/l for white wines samples for Mn), while Ag and Be were the least concentrated. The results indicate that the concentrations measured for all the elements, in white and red wine, are below the limits imposed by Romanian law (Law 244/2002). The variation of Cu content in wine samples is relatively high (Fig. 1), ranging from 25.99 to 2594.79 lg/l. This may originate mainly from Cu accumulation in soil as a consequence of the old practices of using copper sulphate or other copper-based fungicides, to control the vine downy mildew. The frequent use of agrochemicals like pesticides in agricultural practices is a major cause of soil contamination, by trace metal (e.g. copper, nickel, zinc and cadmium) accumulation, which therefore falls ultimately on wine (Medina, 1996). Zn content of studied wines was between 57.44 and 1442.65 lg/l, respectively, with an average of 433.95 lg/l. Ni content ranged between 1.44 and 322.73 lg/l, with an average of 55.15 lg/l, and had the lowest average concentration of 32.14 lg/l for all wines from Moldova region, compared with the other two wine production areas. There are also other elements with high variability, like Mn, but with a framing of concentration values in a well-defined interval per each investigated region. Manganese in small amounts is a natural constituent of grape and wine (Woldemarian & Chadravanshi, 2011); in our study the measured values ranged between 223.5 and 952.5 lg/l for wines
from Muntenia/Valea Calugareasca, 308.37 and 3077.75 lg/l for wines from Dobrogea/Murfatlar, and from 292.73 to 1403.58 lg/l for wines from the Moldova region, respectively. Compared with the other two regions, the wines from Muntenia/Valea Calugareasca have slightly lower amounts of manganese. Strontium was higher in wines from Valea Calugareasca Vineyard, with values ranging from 278.67 to 1600.68 lg/l. 3.1. The content of metals in the wine varieties The comparison of trace-elements concentrations performed by grouping the wines in grape varieties (Fig. 2), reveals a great variation for most of the metals. An overlapping of concentration ranges for the elements analyzed is observed, which make the differentiation on wine varieties inconclusive. Rb and Sr appears to have higher concentration levels in red wines (Rb average 1050.46 lg/l; values between 743.4 and 2066.21 lg/l for 70% of samples; Sr from 410.05 to 1.538.44 lg/l for 70% of samples and an average of 675.06 lg/l for all samples) than in white wines (Rb average783.20 lg/l; values between 219.56 and 884.9 lg/l for 70% of samples; Sr from 144.34 to 489.76 lg/l for 70% of samples and an average of 450.87 lg/l for all samples). Opposite trend, higher concentration values in white wines than in red ones, occurs for Cu and Co. The average value retrieved for cooper was 602.41 lg/l in white wines towards 347.8 lg/l in red wines. For Co, the mean values were 5.63 lg/l for white wines and 2.43 lg/l for red ones, the highest and lowest concentration being encountered in specific Romanian varieties, Columna (42.06 lg/l) and Mamaia (0.68 lg/l), respectively. However, having an overall view to data in Table 2, we do not see a consistent pattern for the elements and it does not seem that on average red wines have higher trace element concentrations than white ones. The opinion that concentration of elements should be significantly higher in red than in white wines due to longer skin contact in the vinification process of red wines is not supported by our data. 3.2. The content of metals in the wine production areas By comparing the mean values across wine producing areas, the concentration ranges of most elements were overlapped (Fig. 3). Nevertheless elements less influenced by the environmental or technological factors such as Mn, Rb, Sr and V exhibit a distinctive concentration pattern across wine regions. The higher mean concentration of Mn and Rb, for both red and white wines, was measured in wines produced in the south-eastern part of Romania, at Dobrogea/Murfatlar region and the lower mean contents in wines from Muntenia/Valea Calugareasca. For the red and
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Fig. 4. Correlation of Mn element with Cr, Sr, Ag and Co to differentiate the origin of wine samples.
white wines, the highest average content of Sr was in the wines from Muntenia/Valea Calugareasca region and the lowest in wines from Moldova, while for V the values were higher for wines produced in Moldova and lower in wines from Muntenia/Valea Calugareasca. Analyzing the results by performing a number of 65 combinations of the measured elements, few possibilities for discrimination by origin were emphasized (Fig. 4). The correlation of Mn with Cr, Sr, Rb, Ag or Co reveals a very good differentiation of wines from
Dobrogea/Murfatlar and Muntenia/Valea Calugareasca regions, while the wines from Moldova show a partial overlap with wines from the other two areas. The correlation of Mn with Cr, Sr, Rb, Ag or Co content in wines (Fig. 4) shows a reasonable degree of discrimination with wine geographical origin. The elemental analysis shows that the geographical origin represents an influencing factor of the wines’ chemical composition, an identification criterion of Romanian wines.
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Fig. 5. Score plot of the three principal components of trace-elements in the Romanian wine samples (n = 60).
Table 5 Experimental results of the elements determined (mg/kg) in soil samples. Sample code
Cr
Mn
V
Co
Ni
Cu
Zn
Valea Calugareasca PS 1.1 PS 1.2 PS 1.3 PS 2.1 PS 2.2 PS 2.3 PS 3.1 PS 3.2 PS 3.3 PS 4.1 PS 4.2 PS 4.3 PS 5.1 PS 5.2 PS 5.3
39.72 40.36 38.68 36.18 34.61 34.63 32.75 35.68 31.65 34.92 39.92 32.72 30.70 34.88 32.07
393.55 381.93 329.73 413.41 331.30 360.33 357.26 311.32 325.19 298.16 282.47 305.71 302.94 215.97 226.37
32.61 32.40 29.36 25.66 24.04 21.82 19.29 18.85 16.62 14.60 14.48 13.61 12.05 11.66 10.26
4.36 3.89 3.40 3.44 2.86 2.58 2.41 2.04 1.92 2.08 1.75 1.52 1.42 1.12 0.94
10.96 11.29 10.38 8.52 7.29 6.38 5.40 5.34 4.74 4.33 3.89 3.38 2.97 2.59 2.12
37.13 12.41 10.23 13.62 8.75 8.89 16.27 5.60 5.88 7.60 5.75 4.94 13.28 4.10 3.07
22.44 24.05 17.82 16.71 14.29 12.98 14.16 11.32 10.68 11.09 9.23 8.51 8.40 6.28 5.37
2.76 2.81 2.46 2.33 2.14 1.86 2.00 1.43 1.22 0.90 0.96 0.92 1.11 0.59 0.44
8.09 4.62 5.62 4.96 4.88 3.35 3.80 3.51 2.58 2.29 3.12 1.67 1.89 1.95 0.91
Murfatlar PS M1 PS M2 PS M3 PS M4
32.24 27.03 26.71 32.10
299.10 282.62 257.20 240.33
7.06 6.41 6.19 5.12
0.74 0.74 0.77 0.49
1.67 1.58 1.48 0.97
5.87 4.18 3.65 2.32
6.72 7.69 7.70 2.98
83.88 0.19 0.09 0.07
2.76 2.20 2.75 2.68
Although metal ions compositional information on Romanian wines is scarce, our results are in agreement with the values reported in literature by other authors (Voica et al., 2009).
As
Rb
Sr
Ag
U
11.28 11.83 8.11 12.18 8.40 10.30 8.53 7.07 7.72 5.21 2.66 4.65 2.73 2.20 2.79
1.13 1.03 1.05 0.93 1.03 0.99 0.92 0.89 0.84 0.66 0.68 0.67 0.64 0.61 0.60
0.84 0.56 0.54 0.49 0.55 0.41 0.41 0.36 0.26 0.15 0.16 0.06 0.08 0.02 0.10
462.31 538.63 511.39 370.05
1.07 0.67 0.69 0.62
0.18 0.02 0.02 0.03
3.3. Principal component analysis The data resulting from the analysis consisted of 12 variables for each of the 60 wine samples. These variables represent the me-
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those of Murfatlar vineyard, while in the soil samples the level of Sr was significantly higher in the Murfatlar region. The concentrations of studied elements decreased with soil sampling depth which means that the surface soil area can be influenced by the environmental contaminants. According to the Romanian legislation (Order 756/1997), the metal concentration levels in studied soil samples show normal values for sensitive soil. 4. Conclusion
Fig. 6. Graphical representation of elements average concentration in soil samples.
tal content profile of each of the wines (Cr, Ni, Rb, Sr, Ag, Zn, Mn, Cu, Co, V, Pb, and Be). We needed to find a correlation between the geographical region and the 12 variables. For this purpose a principal component analysis (PCA) was applied in order to reduce the dimensionality of the data while retaining as much of the variance as possible. The number of principal components is less than or equal to the number of original variables. The PCA was performed using Octave 3.4.3 free software. We chose to represent the first 3 principal components carrying as much as 83% of the variance of the original data. The first principal component PC1 carries 42% of the data variance, the second principal component PC2 carries 27% and the third principal component PC3 14% of the original data variance (Fig. 5). Reducing the data dimensionality from 12 to just 3 while preserving 83% of the variance allows us to represent it in a 3 dimensional space, which is easier to visualize and reveals some clustering with respect to the geographical regions. After plotting the wine samples in the 3 dimensional space, we engulfed all wines from a specific geographic region in a convex envelope in order to better visualize the clustering of the data. The wines from Valea Calugareasca and Murfatlar geographical regions are more easily discriminated while it is more difficult to separate the wines from Moldova.
3.4. The correlation of trace-elements from wines and their provenance soil Additionally, the mineral characterization of soils from Valea Calugareasca and Murfatlar vineyards according to Cr, Mn, V, Co, Ni, Cu, Zn, As, Rb, Sr, Ag, and U content was performed. The obtained results are described in Table 5. The elements Mn and Sr showed the highest level of concentration in the studied soil samples, ranging from 215.97 to 413.41 mg/ kg for Mn and from 2.20 to 538.63 mg/kg for Sr, respectively. The levels of the studied elements were greater from soils of Valea Calugareasca vineyard for almost all the elements with exception of As and Sr (Fig. 6). The values for Sr were significantly higher in soils from the Murfatlar region. We expected to have a correlation between the level of the elements in wine samples and the corresponding soil samples. This hypothesis was applied for the elements Ni, Ag, Be, Cr, Zn, Pb, Co and Cu. We noticed a discrepancy in the case of the highest level of Sr in wine samples from Valea Calugareasca compared with
The elements Mn, Cr, Sr, Ag and Co were identified as indicators for the discrimination between wines and soils of the three major wine-producing regions in Romania. In the case of the wines and soils studied a correlation was observed between the elemental composition of the wine and its provenance soil. This premise is important for the application of the fingerprinting methodology based on multi-element data and statistical analysis for the classification of Romanian wines according to geographical origin. The results of this study constitute the starting point in building a database for Romanian wines. This methodology can be applied for classification of unknown wine according to geographical origin. The methodology will be applied to the other Romanian wine regions for intraregional and inter-regional classification of wines, to extend the elemental characterization of the wine and soil. This classification will help to prevent fraudulent practices in the wine industry. Acknowledgements The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. References Almeida, C. M. R., & Vasconcelos, M. T. S. D. (2003). Multielement composition of wines and their precursors including provenance soil and their potentialities as fingerprints of wine origin. Journal of Agricultural Food Chemistry, 51(16), 4788– 4798. http://dx.doi.org/10.1016/j.foodchem.2008.05.043. Álvarez, M., Moreno, I. M., Jos, Á., Cameán, A. M., & González, A. G. (2007). Differentiation of two Andalusian DO ‘fino’ wines according to their metal content from ICP-OES by using supervised pattern recognition methods. Microchemical Journal, 87, 72–76. http://dx.doi.org/10.1016/j.microc.2007. 05.007. Arvanitoyannis, I. S., Katsota, M. N., Psarra, E. P., Soufleros, E. H., & Kallithraka, K. (1999). Application of quality control methods for assessing wine authenticity. Trends Food Science & Technology, 10, 321–336. http://dx.doi.org/10.1016/ S0924-2244(99)00053-9. Coetzee, P. P., Steffens, F. E., Eiselen, R. J., Augustyn, O. P., Balcaen, L., & Vanhaecke, F. (2005). Multi-element analysis of South African wines by ICP-MS and their classification according to geographical origin. Journal of Agricultural Food Chemistry, 52(13), 5060–5066. http://dx.doi.org/10.1021/jf048268n. Geana, I., Iordache, A., Voica, C., Ionete, R., & Culea, M. (2011). Comparison of three digestion methods for heavy metals determination in soils and sediments materials by ICP-MS technique. Asian Journal of Chemistry, 23(12), 52123–55216. Giaccio, M., & Vicentini, A. (2008). Determination of the geographical origin of wines by means of the mineral content and the stable isotope ratios: a review. Journal of Commodity Science, Technology and Quality, 47(I–IV), 267–284. http:// www.sci.unich.it/~jcs/2008/2008-02-14.pdf. Gonzálvez, A., Llorens, A., Cervera, M. L., Armenta, S., & De La Guardia, M. (2009). Elemental fingerprint of wines from the protected designation of origin Valencia. Food Chemistry, 112, 26–34. http://dx.doi.org/10.1016/j.foodchem. 2008.05.043. Gremaud, G., Quaile, S., Piantini, U., Pfammatter, E., & Corvi, C. (2004). Characterization of Swiss vineyards using isotopic data in combination with trace elements and classical parameters. European Food Research and Technology, 219, 97–104. http://dx.doi.org/10.1007/s00217-004-0919-0. Iordache, A., Geana, I., Ionete, R., & Culea, M. (2010). The optimization of the method for metals content determination in Romanian wines by ICP-MS after microwave mineralization. Progress of Cryogenics and Isotopes Separation, 13(1), 137–145. Jos, Á., Moreno, I., González, A. G., Repetto, G., & Cameán, A. M. (2004). Differentiation of sparkling wines (cava and champagne) according to their
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