Food Chemistry 170 (2015) 138–144

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Food Chemistry

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Analytical Methods

The 87Sr/86Sr strontium isotopic systematics applied to Glera vineyards:A tracer for the geographical origin of the Prosecco

http://dx.doi.org/10.1016/j.foodchem.2014.08.0510308-8146/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel.: +39 0502215707.E-mail address: riccardo.petrini@unipi.it (R. Petrini).

R. Petrini a,⇑, L. Sansone b, F.F. Slejko c, A. Buccianti d, P. Marcuzzo b, D. Tomasi b

a Dipartimento di Scienze della Terra, Università di Pisa, via S. Maria 53, 56126 Pisa, Italyb CRA-VIT, viale XXVIII Aprile 26, 31015 Conegliano, Italyc Dipartimento di Matematica e Geoscienze, Università di Trieste, via Weiss 8, 34127 Trieste, Italyd Dipartimento di Scienze della Terra, Università di Firenze, via La Pira 4, 50121 Firenze, Italy

a r t i c l e i n f o a b s t r a c t

Article history:Received 11 November 2013Received in revised form 18 April 2014Accepted 11 August 2014Available online 20 August 2014

Keywords:Sr-isotope systematicsProsecco wineFood traceabilityGeographical originTIMS

Glera vineyards from the Prosecco wine district in northern Italy have been characterised in terms of the87Sr/86Sr isotope-ratio of musts from the 2010, 2011 and 2012 vintages, coupled with the isotopic anal-ysis of Sr in the labile fraction of the soils of provenance. For a single vineyard, detailed Sr isotopic anal-yses were carried out in sequentially extracted soil fractions at three different depths, and in the grapecomponents (skin, seeds, must and stem), in order to verify the lack of Sr isotopic fractionation withinthe plant. The 87Sr/86Sr in must, seeds and stem overlaps within experimental uncertainties; skins areshifted towards a lower Sr isotopic composition. A large range of Sr isotopic compositions (87Sr/86Srbetween 0.70706 and 0.71215) characterizes musts from the different vineyards, notwithstanding therelatively limited extension of the investigated geographic area. A statistically significant correspondencebetween the soil labile fraction and must is observed.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

The traceability of foods has become a priority amongst con-sumers, driven by the increasing demand for food quality andsafety. In the traceability system, the discrimination of the geo-graphical provenance of food products is essential to verify theclaims of origin often declared on labels, and to prevent unsafeproducts from reaching the consumers. The geographical originassessment of wine is of particular interest, being one of the mostimportant factors that determine its commercial value. UnderEuropean laws, wine production, designation and distribution, aswell as the methods for analysis, are defined by various regulationsand Member States directives (e.g., CEE N. 2676/1990; N. 1493/1999; N. 1623/2000; N. 883/2001; N. 884/2001; N. 753/2002).

For these purposes, a variety of analytical techniques are avail-able, including multi-element and multi-isotope analysis(Gremaud, Quaile, Piantini, Pfammatter, & Corvi, 2004; Kelly,Heaton, & Hoogewerff, 2005; Rodrigues et al., 2011; Suhaj &Korenovska, 2005); biochemical and molecular methods (Luykx &van Ruth, 2008). Amongst these, the application of the Sr isotopesystematics, expressed through the variations of the 87Sr/86Sr ratio,demonstrated to be a valuable tool in tracing the origin of different

agricultural products (e.g., Capo, Stewart, & Chadwick, 1998; DiPaola-Naranjo et al., 2011; Franke et al., 2008; Ghidini et al.,2006; Kawasaki, Oda, & Hirata, 2002; Oda, Kawasaki, & Hirata,2002; Rosner, 2010; Rummel, Höelzl, Horn, Rossmann, & Schlicht,2010), including wine (Almeida & Vasconcelos, 2001, 2003;Almeida & Vasconcelos, 2004; Barbaste, Robinson, Guilfoyle,Medina, & Lobinski, 2002; Durante et al., 2013; Horn, Schaaf,Holbach, Hölzl, & Eschnauer, 1993; Marchionni et al., 2013;Wolff-Boenisch, Todt, & Raczek, 1998).

Strontium behaves similarly to calcium in many geological andbiological processes, having the same valence and similar ionicradius, and can be used as a proxy for labile base cations in tracingthe source and fluxes of soil nutrients in the soil–plant system(Bailey, Hornbeck, Driscoll, & Gaudette, 1996; Capo et al., 1998;Drouet, Herbauts, Gruber, & Demaiffe, 2005). In fact, the 87Sr/86Srratio of plant tissues may be directly related to the cations takenup from the soil, since the isotopic fractionation during this processis assumed to be very small, and anyway it is corrected through anormalisation procedure during analysis. Since the soil mineralogydevelops a characteristic 87Sr/86Sr ratio, which primarily dependson the nature and age of the source rocks, the 87Sr/86Sr ratio ofthe plants can be compared with that of the corresponding soils,and can be used for provenance studies of food. It must be notedthat, in addition to soil exchangeable cations, other Sr sourcesmay contribute to the Sr isotopic composition, as for example

R. Petrini et al. / Food Chemistry 170 (2015) 138–144 139

irrigation water, wet and dry atmospheric deposition and anthro-pogenic fertiliser inputs and the winemaking process itself. Thesecontributions must be considered in the study of provenance(e.g., Green, Bestland, & Walker, 2004).

The ‘‘Prosecco’’ is an Italian white wine labelled with theControlled Designation of Origin (DOC), going from DOC to theDOCG (Guarantee) status in some production areas, within specificproduction guidelines provided by the Italian law (D.Lgs. 17 luglio2009). According to the current production regulations, Glera grapevariety must account for at least 85% of the final Prosecco wineblend, although the Prosecco wine produced in the best, often fam-ily-run, wineries is made of 100% Glera. The varieties permitted forthe remaining 15% include Verdiso, Bianchetta Trevigiana, Perera,Glera Lunga, Chardonnay, Pinot Bianco, Pinot Grigio and Pinot Nero(vinified as white), which in the DOC and DOCG designation of ori-gin must belong to the same geographical area of Glera. The marketdemand for Prosecco wine is internationally rising, exceeding onebillion bottles sold in the last 10 years.

In the present study, the 87Sr/86Sr isotopic systematics wasapplied to soils and Glera musts from the 2010, 2011 and 2012 vin-tages in ten distinct Prosecco vineyard farms in the Veneto Region(Italy), that produce 100% Glera Prosecco wine. The aim was to testthe applicability of the Sr isotopic method to the Prosecco geo-graphic traceability.

2. Materials and methods

Vineyards are located in five distinct catchments in the VenetoRegion alluvial plain, in northern Italy (Fig. 1), and belong to tendistinct farm wineries, trained with different pruning methodsincluding cordon, central curtain, sylvoz and double cane. The mostused rootstocks are Kober 5bb and Richter 110 in clayed soils, and

Fig. 1. Geological sketch map with vineyards locations: (1) Lonigo, (2) Sant’Anna, (3) Pe(10) Nardin-Lison.

the 420A in the more fertile soils. The maximum root growth forvines is at 40–60 cm depth. The pedological features within eachcatchment are quite homogeneous, with substrates characterisedby recent fine sediments. From a geological point of view, the sed-iment age ranges from Pleistocene to Holocene and in one site thesubstrate is constituted by volcaniclastic deposits (Lonigo farm).The climatology of the Region is characterised by low winds fromthe easterly and north-easterly sectors; precipitations in generalincrease northwards (from about 700 to 1100 mm/years, on aver-age), having moderate accumulation patterns in the Plain that risestowards the first orographic barriers and with warm-humid flowsimpinging on the pre-Alpine chain (Barbi, Monai, Racca, & Rossa,2012).

Glera grape samples were collected by hand during the 2010,2011, and 2012 grape harvests, each time from the same plants,and frozen to prevent fermentation. Average productivities (±1r),referred to the 2010 harvest, are 22.5 ± 8.9 grape clusters per vine,with a weight of 295 ± 99 grams per cluster, and a total net produc-tion value of 5.3 ± 1.9 kg of grape per vine. Average sugar contentwas 16.6 ± 1.5 �Brix, the must mean pH was 3.3 ± 0.1, and totalacidity 6.9 ± 0.8 g/L.

Grape components (must, skin, seeds, and stem) for isotopicanalyses were separated with gloved hands after thawing. For Srisotopic analysis, both chemical mineralisation and thermal ashingprocedures were tested. Chemical mineralisation was carried outby high pressure digestion of about 5 g of sample, previously driedin a forced-air oven at 60 �C for 12 h, placed in a pre-cleanedscrew-capped Teflon beaker, added with 5 mL of concentratedultrapure HNO3 and H2O2 and heated at 120 �C for 2 h. The residuewas then dried at 60 �C. The procedure was repeated until com-plete destruction of the organic matter; the residue was dissolvedwith ultrapure 2.5 N HCl. For thermal ashing, about 8 g of sample

raro, (4) Broscagin, (5) Braga, (6) Pattarello, (7) Bottazzo, (8) Gaiarine, (9) Aleandri,

140 R. Petrini et al. / Food Chemistry 170 (2015) 138–144

were first dried at 60 �C in a clean nickel crucible, and then placedin a muffle furnace, gradually increasing the temperature and leav-ing at 700 �C for 1 h. Ashes were then dissolved with ultrapure 2.5N HCl. Chemical oxidation generally yielded lower instrumentalerrors during Sr-isotopic data acquisition, but thermal ashingproved to be more efficient and rapid in destroying the organicmatter, so it was the preferred method. Repeated isotopic analyseson samples processed with both thermal ashing and acid-digestionmethods gave identical results within experimental uncertainties.

Soil samples were collected using stainless steel shovels at threedifferent depth intervals (0–20; 20–40; 40–60 cm), taking care toavoid mixing between the different horizons. Samples were air-dried, in order to facilitate homogenisation during sub-sampling(Ure, 1994). The labile fraction of Sr for Sr-isotopes analysis wasextracted from 10 g of the 2 mm-sieved fraction of soil by adding50 ml of 1 M ammonium acetate (NH4OAc) ultrapure grade bufferedat circum-neutral pH. NH4OAc is a widely employed reagent forleaching the exchangeable fraction of metals from soils in sequen-tial chemical extraction schemes (Filgueiras, Lavilla, & Bendicho,2002, for a review; Gómez Ariza, Giráldez, Sánchez-Rodas, &Morales, 2000). In particular, it has been found that NH4OAc at neu-tral or slightly alkaline pH provides a good estimation for the plant-available alkali and alkaline earth cations in soils, adsorbed on claysor bound to carbonates (e.g., Heald, Menzel, Roberts, & Frere, 1965;Johnson & Goulding, 1990; Simard, 1993). The suspension wasstirred for one hour, let to settle and filtered through 0.45 lm nylonfilters. The Sr owing to dissolution of carbonates was obtained in asequential extraction step, following the NH4OAc step, using 1 MHCl that has the sufficient buffering capacity to dissolve carbonatesbut limited or no impact on clays; the bulk soil was obtained by totaldigestion using a mixture of ultrapure HF+HNO3 in screw-cappedTeflon beakers at 100 �C followed by ultrapure HCl dissolution ofthe residue. Sr was separated from isobar by cation-exchange chro-matography using Biorad AG 50 W X8 resins (200–400 mesh) andultrapure HCl as eluent; the total procedural blank was less than30 pg. Isotopic analyses were carried out by thermal ionisation massspectrometry (TIMS), using a VG 54 mass spectrometer. About 1 lgof Sr(NO3)2 was loaded on a tungsten filament for thermal ionisa-tion, using TaCl5 as an emitter. The measured 87Sr/86Sr ratios werefractionation-corrected to 86Sr/88Sr = 0.1194; experimental uncer-tainties represent in-run statistics at 2-r confidence level. Repeatedanalyses of the certified isotopic reference material NIST SRM 987gave an average value of 87Sr/86Sr = 0.71025 ± 0.00002 (2-r;n = 25). This value overlaps what reported in the current literature(Balcaen, De Schrijver, Moens, & Vanhaecke, 2005; Faure &Mensing, 2005), and is usually taken as a reference for Sr isotopicanalysis on wine and food (Crittenden et al., 2007; Durante et al.,2013; Marchionni et al., 2013; Rummel et al., 2010). No correctionsfor instrumental bias were applied to the measured ratios. The lackof 87Rb was carefully checked before starting data acquisition andduring each run.

Soil mineralogy was determined by X-ray powder diffraction(XRD), using a Siemens D500 diffractometer, with the CuKa radia-tion (wavelength: 1.54 Å), monochromatized with a flat graphitecrystal.

3. Results and discussion

3.1. Mineralogy and Sr-isotopes of soils

All soils are fine-grained, belonging to the mud- and sandymud-facies, and contain clays, carbonates and silicates as majorphases. The more abundant mineral phases are quartz, calcite,dolomite, feldspar and clay minerals; representative XRD patternsof soils from the different basins are shown in Fig. 2 for the 40–60 cm depth interval. It is well known that clays have an active role

as cation exchangers in the dynamics of element transfer in thewater–soil–plant system, leaving the exchangeable fraction avail-able for plant uptake (e.g., Comerford, 2005). The reactivity of car-bonates in soils through dissolution reactions also determines therelease and bioavailability of ions, mostly the Ca2+ and Mg2+ basiccations and Sr2+.

The change in the 87Sr/86Sr ratio as a function of the sequentialextraction and soil depth has been explored in a single vineyard(Nardin-Lison vineyard), taken as test site. The results are reportedin Table 1. A clear distinction in the Sr isotopic composition of theextracted fractions is observed: in particular, HCl-soluble fractionshave the lowest Sr isotopic ratio (87Sr/86Sr = 0.70815); the bulk soilis significantly more radiogenic (87Sr/86Sr in the range between0.71598 and 0.71642), indicating the contribution of Al-silicateminerals with high 87Sr/86Sr isotope-ratios and likely high Rb/Srelement ratios. The ammonium acetate extracted fraction is char-acterised by an intermediate isotopic value of 87Sr/86Sr in the rangebetween 0.70914 and 0.70955.

3.2. Sr isotopes variability in vine components: the example of theNardin-Lison vineyard

The different vine components were analysed for Sr isotopes atthe Nardin-Lison vineyard, considering the 2010 harvest. The87Sr/86Sr values obtained from single runs in must (0.70977 ±0.00003), seeds (0.70973 ± 0.00004) and stem (0.70978 ± 0.00004)overlap within analytical uncertainty (expressed as 2-r), indicatingthat no detectable fractionation occurs amongst the different partsof the vine plant. The only exception is the Sr isotopic compositionof skins, which is characterised by a slightly lower ratio(87Sr/86Sr = 0.70965 ± 0.00003). This might reflect atmosphericdeposition or the local surface treatments on vines with fungicides;in particular the 87Sr/86Sr ratio measured in this study on copper-based fungicides registered for agricultural use yielded a value aslow as 0.70769 ± 0.00002. On the basis of these results, the Sr isoto-pic value of must has been considered as representative of thewhole vine plant, and therefore used to investigate the vine-soilcorrelations for all the studied vineyards.

Furthermore, in order to evaluate if the sampled vine plantswere actually representative at the vineyard scale, musts weremeasured in vines collected during 2012 in different sites fromthe Nardin-Lison vineyard. Overall, the data indicate very low iso-topic variability (average 87Sr/86Sr = 0.70925 ± 0.00004, 2-r). Thissmall variability has been hypothesized to characterise all thevineyards of the study.

3.3. Sr-isotope ratio for the different harvests and soil-must correlation

The Sr isotopic composition measured in musts from grapesharvested in 2010, 2011 and 2012 and the 87Sr/86Sr ratio of theammonium acetate soluble (labile) fractions of the correspondingsoils at the depth of the maximum development of grapevine roots(40–60 cm) are reported in Table 2.

The Sr-isotope ratio measured in soil leachates shows a largevariability between the different vineyards, ranging between0.70772 and 0.71097, widely exceeding the experimental uncer-tainty and reflecting the distinct geological sources for the sedi-ments; in particular, the lower radiogenic component isconsistent with the isotopic signature of Tertiary volcanites(Macera et al., 2003), whilst the highest 87Sr/86Sr ratio reflects the87Sr enriched character inherited from the different crustal compo-nents outcropping in the geologically complex Alpine domain, andincluding lithologies of different age and origin.

A general correlation is observed amongst musts from the dif-ferent harvests of the same vineyard, as shown by the quite goodcorrelation between the 2010 vs 2011 (R2 = 0.97) and between

Fig. 2. Representative XRD spectra of soils, 40–60 cm depth. (a) Lonigo; (b) Pattarello; (c) Braga; (d) Bottazzo; (e) Nardin-Lison. Abbreviations: mont/chl: montmorillonite/chlorite; illite/mus: illite/muscovite; kaol: kaolinite; qz: quartz; feld: K-feldspar; cal: calcite; dol: dolomite.

Table 1Sr isotopic compositions of soil fractions.

Nardin-Lison vineyard 87Sr/86Sr 2-r

Soil 0–20 cm labile fraction 0.70914 0.00002Soil 20–40 cm labile fraction 0.70925 0.00003Soil 40–60 cm labile fraction 0.70955 0.00004

Soil 0–20 cm carbonate fraction 0.70815 0.00004Soil 20–40 cm carbonate fraction 0.70815 0.00003Soil 40–60 cm carbonate fraction 0.70815 0.00003

Soil 0–20 cm bulk 0.71598 0.00004Soil 20–40 cm bulk 0.71558 0.00005Soil 40–60 cm bulk 0.71642 0.00003

R. Petrini et al. / Food Chemistry 170 (2015) 138–144 141

the 2010 vs 2012 (R2 = 0.90) harvests isotopic data. In order to fur-ther quantify these observations and to highlight the possible rela-tionships between soils and the corresponding musts, a statisticalapproach was followed. In the cumulative frequency data retriev-ing, soils and musts belonging to the 2010, 2011 and 2012 harvestsfollow a normal distribution. The application of the different statis-tical tests for normality (Anderson–Darling, Kolmogorov–Smirnov

and similar) confirms this result with p� 0.05. This indicates thatfor the 87Sr/86Sr isotope-ratio of the soil leachates and musts theGauss model can be taken as representative, revealing that vari-ability is well equilibrated around a defined barycenter and thatthis behaviour is maintained over the time. A comparative analysisof the variability, graphically visualised by notched box plots(Fig. 3), shows that there are no significant differences betweenthe median (mean) Sr isotopic compositions of soils and musts,qualitatively suggesting that must reflects the Sr-isotope ratio ofthe corresponding soil. In this case too, the application of the med-ian test confirms the result with p� 0.05.

To better assess this correspondence, and to identify a cause-effect relationship between soils and musts, linear fittings weremodelled assuming the Sr isotopic composition of soils as the inde-pendent variable (the source), and the must as the dependent var-iable; the prediction and confidence bands arising from regressionanalysis were also reported together with the x = y line (Fig. 4).

The following regression equations were obtained for the isoto-pic ratio of must from soil:

87Sr=86Sr ðmust 2010Þ ¼ �0:133þ 1:188 � 87Sr=86Sr ðsoilÞR2 ¼ 0:77

Table 2Sr isotopic compositions of musts and soil labile fractions.

Vineyard must 2010 2-r must 2011 2-r must 2012 2-r Soil labile fraction 2-r

Lonigo 0.70706 0.00003 0.70718 0.00005 0.70725 0.00004 0.70772 0.00004S. Anna 0.71049 0.00003 0.71059 0.00003 0.71093 0.00002 0.71097 0.00004Peraro 0.70947 0.00003 0.70993 0.00004 0.70965 0.00004 0.70899 0.00004Broscagin 0.70907 0.00003 0.70964 0.00002 0.71005 0.00005 0.70880 0.00003Braga 0.71066 0.00003 0.71100 0.00005 0.71043 0.00003 0.70986 0.00004Pattarello 0.71215 0.00003 0.71266 0.00002 0.71219 0.00004 0.71064 0.00003Bottazzo 0.70919 0.00004 0.70941 0.00003 0.70947 0.00005 0.70941 0.00003Gaiarine 0.70919 0.00004 0.70924 0.00005 0.70891 0.00003 0.70839 0.00004Aleandri 0.71022 0.00003 0.71097 0.00003 0.71021 0.00004 0.70956 0.00003Nardin-Lison 0.70977 0.00003 0.70981 0.00004 0.70921 0.00002 0.70955 0.00004

Fig. 3. Notched box plots for the Sr isotopic composition of the soil ammoniumextracted fraction and for the 2010, 2011, and 2012 harvests must. In a notched boxplot the notches represent a robust estimate of the uncertainty about the mediansfor box-to-box comparison. Boxes whose notches do not overlap indicate that themedians of the two groups differ at the 5% significance level. Black points areanomalous values.

Fig. 4. Linear regression between the soil labile fraction (x) and the grape must (y)of 2010, 2011, and 2012 vintages (a, b, and c, respectively). Internal (dashed) curvesare the confidence bands defining the area that has a 95% chance of containing thetrue regression line. External curves represent the prediction band, that is the areain which 95% of all data points are expected to fall. The x = y line is also reported.

142 R. Petrini et al. / Food Chemistry 170 (2015) 138–144

87Sr=86Sr ðmust 2011Þ ¼ �0:161þ 1:227 � 87Sr=86Sr ðsoilÞR2 ¼ 0:71

87Sr=86Sr ðmust 2012Þ ¼ �0:127þ 1:180 � 87Sr=86Sr ðsoilÞR2 ¼ 0:78

The prediction bands define the area in which 95% of all datapoints are expected to fall. In contrast, the 95% confidence bandsidentify the area having a 95% chance of containing the true regres-sion line. Thus, given the assumptions of linear regression, we canbe 95% confident that the confidence bands enclose the true best-fit linear regression line, leaving a 5% chance that the true line isoutside those boundaries. This is not the same as saying it will con-tain 95% of the data points and, in fact, some of them are locatedoutside, even if all are inside the prediction bands.

By considering the R2 values it is possible to say that: (1) from71% (2011) to 78% (2012) of the total variance characterising themusts is explained by variation in soils, and (2) the complementaryvalues indicate that some other source of variation affects themusts. These may be represented by Sr inputs with a typical isoto-pic signature from deficit irrigation, organic matter supply and soilmanagement.

In addition, the parameters of the different fittings are quitesimilar, with a wide overlapping of the 95% confidence intervalsfor intercept and slope, supporting the conclusion that the mustisotopic composition actually depends on the soil characteristicvalues.

It is important to note that the previous approach is classical andassumes that x (soil labile fraction) is known without error, whilst y(must of the different years) is a measured value subject to uncer-tainty. However in our case both values are subject to uncertainty

R. Petrini et al. / Food Chemistry 170 (2015) 138–144 143

and the linear Deming regression (Deming, 1943) could be a moreadequate method. In this regression technique the errors on x andy are considered independent and the ratio of their variance knownand equal to 1 when the measurement method is the same or, alter-natively, different from 1 when the variance of the errors can beestimated. The ratio represents a correction factor able to take intoaccount the presence of errors in both variables and affects the esti-mate of slope and intercept. The results indicate that slope andintercept are quite similar for the years 2010 and 2012 (slope1.35 and 1.34, respectively, intercept �0.25 and �0.24, respec-tively), whilst a clear difference is revealed for 2011 (slope 1.46,intercept�0.33), a phenomenon attributable to the higher variationcoefficient (ratio between mean and standard deviation) of this setof data. In this case too the 95% confidence interval presents wideoverlapping both for the intercept and slope confirming the depen-dence of the isotopic ratio of must on that of soil.

Finally, due to the presence of two anomalous values for the iso-topic composition of the must of 2010 and 2012, related to Lonigofactory (Fig. 3), the robust linear regression was also applied.Robust methods are developed because atypical observations in adata set heavily affect the classical estimates. The ordinary leastsquare estimator minimizes the sum of the squared residuals,but is very sensitive to outliers. The least trimmed squares estima-tor (Rousseeuw, 1984; Verboven & Hubert, 2005) minimizes thesum of the h smallest residuals with h ranging from n/2 and n(with n the number of measurements). The results again confirmthe good performance of the regression, with a value of R2

equal to 0.89 for the relationship between soils and 2010 must(intercept = �0.38, slope = 1.53), 0.88 for soils and 2011must (intercept = �0.46, slope = 1.65) and 0.78 for soils and 2012must (intercept = �0.13, slope = 1.18).

Summarising, it is possible to say that the confidence intervalsof intercept and slope obtained by the three different methodspresent wide overlap. On this ground, the Sr isotopic compositionis a good tracer of must origin, provided that a soil-extraction pro-cedure with ammonium acetate is applied to recover the bioavail-able Sr fraction. The different pruning methods do not seem tocause significant changes in absorption processes by plants, asfar as Sr isotopic systematics is concerned, nor does the use of dif-ferent rootstocks. However, additional studies would be necessaryto confirm this observation.

4. Conclusions

The Sr-isotopic systematics was applied to must from differentGlera vineyards in the Veneto Region (Italy), in order to test theapplication of the method in the traceability of the Prosecco wine.The analysis was focused on must, in order to avoid the possibleinfluence of the vinification process, that will be investigated in fur-ther studies.

The results show that the ammonium acetate extracts fromsoils from the Prosecco vineyards, intended as representative ofthe labile fraction, are characterised by a large Sr isotopic variabil-ity. Musts from the different vineyards are also characterised byvariable 87Sr/86Sr ratio, which remains reproducible in the differentharvests. For each vineyard, the Sr-isotope ratio in must and that ofthe labile fraction in the corresponding soil are correlated withinexperimental uncertainty, indicating that the isotopic compositionin must can be forecast on the basis of that of soil. These observa-tions confirm that the Sr-isotope systematics may be a potentialtool in discovering fraud in wine trade.

Acknowledgements

The Authors wish to thank the Aleandri, Bottazzo, Braga,Broscagin, Gaiarine, Lonigo, Nardin-Lison, Pattarello, Peraro and

S. Anna wineries for allowing sample collection; U. Aviani helpedwith the isotopic data acquisition, C. Vaccaro is thanked for usefuldiscussions. The ‘‘Accademia Italiana della Vite e del Vino’’ is alsothanked. Critical comments of two anonymous Reviewersimproved an early version of the MS, and are sincerelyacknowledged.

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