TRACE FINGERPRINTING

TRACE ELEMENT FINGERPRINTING OF CANADIAN WINES

by Vivien Taylor

A thesis submiued totheSchool of Graduate Studies in partial fulfilment of the requirements forthedegree of Master of Science

Departmentof Environmental Scimce Memorial University of Newfoundland

October.2001

St. John's, Newfoundland

Abslnd

Wines from Canada'sIWomajor winegrapegrowing regions.. the Niagara Peninsula, Ontarioand theOkanagan Valley, British Columbia...~fingerprinled wilh 100'% corTeC1c1assificaljon. usingthc e1emcnIsAt. V, Mn.Co,Zn,Sr, Rb, Mo. Sb,andU.

for the purpose of verifying region oforigin. Wines were diluted 2:1 wilh 0.2 M HNO_l and element roncenbations in wine weredeterminedbyinductively coupled plasma mass spectrometry (ICP-MS), wilh precision <5% for Cd. Sb,Ba. 11.Ph, and U; <10% forAs., Rb, Sr, Mo. Cs.La.Ce. and Th; <15% for V. Mn. Fe. Cu.Zn.Ag.andBi;<20%for Mg.

AI. Ca, Co, Ni,andBr. and <25% forti,Be, Ti. Se. and I;and27% for CIandP.

Element concentralions were log lnlnsfonned10give a better evalualion of Ihe consistency of thedatagiventheassumptions evolved inparametricstatiSlical models.

Graphical analysis and multivariate statistics wereusedtodiscriminate: ""inebyregion,and theelement Sr was found to havethehighest di5Criminating power. Analysis of vineyard soils by X-ray nuorescence also revealed that Sr. as well as Ca, Ba,and Ti. can be used to discriminate soils fromtheIWoregions unequivocally. Notetherelalionship between soil and wine concentrations wasnotlinear. Elements in winegroupedby principal component analysis showed agreement wilh elements groupedbyionicpolential. suggesting element mobility has a strong influence on element concentrations in wine. Discriminant and cluster analysis oftheOkanagan wines grouped wines made from grapes fromthe same vineyard toa highdegree,suggesling individual vineyards couldbefingerprinled for this region.

TheNiagara wines were groupedtoa lesser extentbythesestatistical procedures. possibly due10themOfC homogeneous geologyandclimate oftheNiagara region.

Theauthor would liketothankPro[ Henry Longerich for advice and encouragement. aswdlas formonetarysupponandtheopportunitytoattend two conferences.Dr.John Greenough provided roomandboard during field work. as well as monetary support.andgave much needed explanations of geology. Lakmali Hewa. Mike Tubren.andPam King were extremely generouswiththeir timeand3dvice inlhe laboratory.I thankmy academic committee.Dr.Paul SylvesterandDr.Moire Wadleigh.

who gave helpful advice.andDr.Niall Gogan. for overseeingthecourse work ponion of my degree. The School of Graduate Studies and Prof. Henry Longerich provideda fellowship fortwoyean of financiaJsupport..

iii

Abstract.

Acknowledgments . Table ofContent5 list ofTables . list of Figures .

. ii

. .... iii ... iv . .... viii ..xi lisl of AbbreviationsandSymbols Used ..

Chapter IIntroduction.

1.1 Fingerprinting wines . 1.2 Major wine regions in Canada.

1.3Grape growing in Canada . 1.4Wine production inCanada . I.S Wine labelling .

1.6 Objectives and outline ofthestudy.

. .. xvi

. 1·1

. .. 1-1 '·2 ..1-3 ..1-4 . .. 1-6 . 1·7

Chapter2 Analytical Methodology. . 2-1

2.1 Wine sampling. . 2-1

2.2 Wine analysis. . 2-2

2.2.1 Reagentsandsolutions . .2-2

2.2.2 Sample preparation . . .... 2-2

2.2.3 Calibrationstandardsand reference materials . . . 2·3

2.2.4 Instrumentation .. 2-3

2.2.5 Operating conditions. . ... 2-4

2.2.6 Background ion intensities .. 2-7

2.2.7Choia:ofisotopc:s. .2-8

2.2.8Analogcalibration. ..2·9

2.2.9 Data acquisition .2·10

2.3Calculation ofunknown element concentr.uions . . 2-11 2.3.1 Detection limits and precision. as relative standard deviation

(RSD) calculated foreachelement . . .. 2-12

2.3.2 Accuracy ofthemethod . ..2-13

i,

2.4lcemnes . . 2-15

2.5 Wine digests. . 2-16

2.5.1 Digesc.ionpt'OCl:rlure . . 2-17

2.5.2 AnalysisbyICP-MS . . 2-17

2.6Multivariateanalysis. . 2-19

2.6.1 Application of statisticstochemicaldata . .2-19 2.6.2 Considerations for analytical uncenainty . . 2-20 2.6.3 Graphical examination ofthc:data . . 2-20

2.6.4 Multivariate methods . . 2-21

2.6.5 Principal component analysis(PeA) . . 2-22 2.6.5.1 Elements discarded fromthe:PeA . . ..2-24 2.6.6 Cluster analysis. . . .. . .. . . . .. . .... 2-26 2.6.7 Discriminant analysis. . . • . . . . 2-26

2.7 Soil samplingandanalysis . 2-28

2.7.1 Vineyard soil sampling. .. . .. .. .. .. .•. . . 2-28

2.7.2 Soil sample preparation. . . .. 2-29

2.7.3 Measurement of soil pH . 2-30

2.7.4 Measurement of soil conductivity .. 2-31

2.7.5Soil chemical analysis. . . .. . . ..• . . 2-31

Chapter 3 Results of Analyses. 3 - 1

3.1 Detenninedconcentrations of elementsin¥lines .. 3 - 1 3.2logtransformation. .. . . • • . . . 3 . 1

3.3 Principal component analysis. . . 3 - 3

3.3.1 Pearson's R cotTelation matrix. . 3 - 3

3.3.2 Derivation of principal components. . 3 - 3

3.4 Cluster analysis . . 3 - 4

3.5 Regional fingerprint. . 3 - 4

3.5.1 Discriminant analysis oftheprincipal components . . 3 - 5 3.5.2 Discriminant analysis ofelement concentrations . . 3 - 5 3.6 Inclusion ofFrenc:h wines inthefingerprint. . 3 - 7 3.6.1 Inclusion of French wines in lhe PCA . . 3 - 7 3.6.2 Discriminant analysis of French. Okanagan.3tIdNiagara wines 3 - 7

3.7 Fingerprinting individual vineyards. . 3 -9

3.7.1 Fingerprints of individual Okanagan vineyards. . .... 3-9 3.7.2ClusteranalysisofaiIOkanaganwinessa!npled. 3-11 3.7.3 Fingerprints of individual Niagara vineyards. . 3 -II 3.7.4 Cluster analysis of all Niagara wines sampled. . 3 - 12 3.8 Discrimination of colour and variety. . ... 3 - 12 3.9 Detemincd concentrations of elements. pH.and conductivity of vineyard

soils. . .... 3-13

3.9. I Correlations between element concentrations in wine and soil 3 - 13 3.9.2 Correlations between element ratios in soilsandwines 3 - 14

3.10 Swnmary . . 3 -15

Chapler4Fingerprinting ofCanadian wines ...•.•.•.•

4.1 Introduction.

4.2 Ionic potential of elements 4.3 Interpretation ofPeA . 4.4 Discrimination of region . 4.5 Composition ofOkanaganwines . 4.6 Composition ofNiagan. wines .

4.7 Relationshipbetweenelement concentralions in soilsand wines . 4.8 Summary .

.. .. 4-1 . .... 4-1 4-1 ..4-3 ...4-5 ..4-7 .. .. 4-8 ... 4-9 .. .. 4-10

. 5·1

.5-1 ... 5-1 ... 5-3 ..5-5 ... 5-5 ...5-6 .. .... 5-6 .. 5-6 ..5-7 .. 5·7 .. 5-8 .... 5·g Chapter 5 Soil, geologyandwine .

5.1 Introduction .

5.1.1 Overview-oCthe geology of theOkanaganVaney . 5.1.2 Overview of the geology of the Niagara Peninsula ..

5.1.3 Differences in climate between the two regions . 5.1.4 Soils of the Okanagan Valley andtheNiagara Peninsula . 5.2 Examination ofthesoilandwinedata .

5.2.1 Comparisoo of NiagaraandOkanagan soils.

5.2.2 Composition ofOkanagan soils .

5.2.3 Element concentrations in Okanagan wine and soil.

5.2.3.1 Concentrations ofU in Southern Okanagan wines 5.2.3.2 Concentrations of elements in Peachland wines . 5.2.3.3 Concenlrations of As and Pb in wines and soils . 5.2.3.4 Concentrations of elements in winesandsoils from

Okanagan Falls. . . . 5-8

5.3 Discussions . . 5-9

5.3.1 Interpretation of soil analyses. . ... 5-9 5.3.2 Soil composition in the Okanagan Valley. . 5·10 5.3.3 Element concentrations in Okanagan Valley soilsandwines 5·11

5.3.3.1 Uranium deposits . . 5-12

5.3.3.2 PossiMe sources ofNi. Mo.Zn.Cd.andCu in Peachland

wines . .5-13

5.3.3.3 Anthropogenic sourcesofPband Asin soilsandwines5-14 5.3.3.4 Geologic heterogeneity of tile Okanagan Falls area. . 5-15

5.4 Summary . 5-16

vi

Chapter 6 Conclusions. . 6-1 6.1 Summary of fingerprinting results and implications . . 6-1 6.2 Sowcesofthe fingerprintandsuggestions for funher study 6-1

6.3Conclusions. . . 6-3

References . . R-I

AppendixI:Calculation of element concentration(ppb)fromsignal intensity (cps) for

wines analysed by ICP-MS . .. A-I

Appendix 2: Element concentrations in wines (ppb) . A-8

Appendix 3: Element concentrations in wines preparedbydigestionmelhod ...A-32 Appendix4:Pearson'sRcorrelation coefficients between element concentrations

in wines . . A-J5

Appendix 5 : Element concentrations in vineyard soils. . A-38 Appendix 6 : Pearson's R correlation coefficientsbetweenelement concentrations in wines

andsoils. A-51

vii

LiltofTabla

Table 2.1 Element concentrations in calibration standards for wine analysis 2-35 Table 2.2 Operating conditions oftheinductively coupled plasma mass spectrometer fot

wineanal~is.. . 2-33

Table 2.3 Limit ofdetection. limit of quantization and relativestandarddeviation(RSD)

for elements determined in wine samples. . . 2-36

Table 2.4 Recommendedvalue(RV). most probable value (MPV)andmean detennined element concentrations. with standard deviations. for waten reference materials

(allconcentrations in ppb). . .... 2-37

Table 2.5Mean concentrations (inwt%for oxides.ppmfor elements) andstandard deviations of soilsfromtenNiagara and ten Okanagan vineyards; andmeanratio ofconcentration from 2mm fraction to 0.074 mm fraction for each region .. 2-39 Table 2.6 Ratio of element concentrations of10soils analysedby fusedbead analysis

relative to concentrationsfrompressed pellet analysis . . 2-40 Table 3.1 Minimumandmaximum concentrations (Ppb) of elements measured in this study

of CanadianwinecomparedtoCanadian Drinking Water Guidelines (Canadian

Task Force on Water. 1993). .. ... )-16

Table 3.2 Principal component analysis for elements in wines using 10 componentsand

VARIMAX rotation. . )-17

Table 3.3 Elements grouped by principal components andthepercent of total variance

each component explains. . 3-18

Table 3.4 Group meansandclassification matrices of wine regions discriminated by

principal components (equation 3.1). . . )-18

Tab!e 3.5 Discriminating PO\l,'er. as F-to-remove statisticsandtolerance. ofPCA componentsusedto discriminate wineregions _ ....3-18 Table 3.6 Test statistics for discriminant analysis of wines region using PeA

components . . 3-19

viii

Table 3.7 Groupmeansand classification matrices for discriminant analysis ofNiagarn. and Okanagan winesusing element conc:enll3tions (equation3.2) . . . 3-19 Table3.8 Discriminatingpower. as F-Io-removestalislic andtolerance. ofelementsused

to discriminateNiagaraandOkanaganwine . . 3·19

Table3.9Test statistics for discriminant analysis of Niagara and Okanagan wines. .3-20 Table3.10Groupmeans andclassificationmatricesfor discriminant analysis ofNiagarn..

Okanagan. andFrenchwines. . . 3-20

Table3.11Discriminating power. as F-to-remove statisticsandtolerance. of elements used to discriminate French. Niagara and Okanaganwines . . . 3-20 Table3.12Test statistics for discriminant analysis of Niagara. Okanaganand French

wines . . 3-20

Table3.13Discriminant functionsandassociated eigenvalues for classifyingwinesfrom5

Okanagan vineyards. 3-2t

Table3.14Group means for discriminant functions ofwines from 5Okanagan

vineyards. . . 3·21

Table3.15Classification matrices for discrimination of wines from5Okanagan

vineyards . . 3-22

Table 3.16 Discriminating power.asF-to-remove statisticsandtolerance. for elements usedto discriminate winesfrom 5Okanagan vineyards. . . J·22 Table J.17 Test statistics forthediscriminant analysis ofwiocs from 5 Okanagan

vineyards. . J·22

Table3.18Concentrations of select elements in icewines. late harvest wines and Rieslings

from Lang vineyardsandSummerhillEstates. .3-23

Table3.19Discriminant functions and associated eigenvaluesfoewinesfrom4

Niagara vineyards. .. . 3·23

Table3.20Group means for discriminant functions of wines from 4 Niagara

vineyards. . . 3-23

ix

Table 3.21 Discriminating power. as F-to-remove statisticsandtolerance, for elements usedtodiscriminate wines from5Niagara vineyards. . }·24 Table 3.22 ClassifICation matrices for discrimination of wines from 5 Niagara

vineyards . . . 3·24

Table 3.23 Tesl.Slatistics for!hediscriminant analysis of wines from 5 Niagara

vineyards. ..3·24

Table 3.24 Positive Pearson's R correlation coefflCienlSbetweenwinesand soils detennined for concenlIalion ratios ofelements . . . 3·25 Table 3.19 Classification matrices for discriminant analysis of Niagara wine by vineyard.

... 3-48 Table 3.20 Tesl statistics for!hediscriminant analysis of Niagara wine by vineyard. 3-49 Table 5.1 T·tes! statistics (using separate variance T.test)andassociated probabilities for

soil analytes which are significanlly different betweentheOkanagan and Niagarn regions. as well as mean concentrations ofeach analyte in the two regions . 5·17 Table 5.2 T-test statistics (using separate variance T-tCSI)and associated probabilities for

Srand Tiin OkanaganandNiagara wines. as well as mean concentrationswith

standard deviation (SO) for eachregion. . .5·17

Table 5.3 Concentrations of AsandPb in soil (in ppm) and wine (in ppb) of vineyards

knownto haveonce: been apple orchards. . 5·17

Table 5.4 Mean conc::enlralionandstandard deviation oftheelemenlS U. Pb. Bi.andTh in theOkanagan Falls wines. WildGooseandStag's Hollow. . . .... 5-18

List

or

Fiprn

Fig.2.1 RSD of determined element concentrations in Irvine ChardonnayfromJuly7.8.9 analyses. and from July7. July 30 and August 30 analyses. . . ..2-41 Fig.2.2 Oxide formation as a function ofnebuliser gas flow-rate (Vmin) forTh. . .2-42 Fig.2.3 Oxide fonnation as a function of ncbuliser gas flow-rate (Umin) for Co. . .. 2-43 Fig.2.4Oxide fonnation and double-ehargcd oxide formationas a function of nebuliscr

gas Oow-f'3te for Ce. . .2-43

Fig.2.5IonintensitiesfOC'lowmass elements (Ca. Mn. Cu) as a function of ncbuliser gas

flow-rate (Umin). . . .2-43

Fig.2.6 Low mass range ion intensities (counts per second (cps» detennined for blank solutions of 6% ethanol (in 0.1 M HNO}), 0.2 M HNOjandH~O.. . .. 2-44 Fig.2.7 Ion intensities (cps)fOC'kJw mass elements in two wines (Irvine Chardonnay and

DeSousa Baco Noir)and6% ethanol blank solution. all diluted to 0.1 M HNO).

... 2-45 Fig.2.8 Ion intensities 0(20 ppm CI (35 and 37 amu) and 0.16 ppm Dr (79 and 81 amu)

and the polyatomic species llC)7CI (49 amu), J'CIItO(51 amu),.MlM'CI(75 amu), .10A.-J⁷CI(77 amu), and IIBrlH (82 amu) compared to the blank solution... 2-45 Fig.2.9 Ion intensities (cps) of 15ppmCa and the polyatomic compounds it fonns at57,

59. and 65 amu. asco~to a6% ethanol blank.

.. 2-46 Fig. 2.10 Nonnaliscd sensitivity calculated as cps/concentration/isotopic abundance

(equation 2.9). . .1-46

Fig.2.11 Two isomcrsofthe fructose molecule:a.D-<-)-fructoscandD-<+

fructose. .2-47

Fig.l.l2 Fractionation offruetosc to fonn molecules with mass121 atomic mass units

(amu) and59 amu . 2-47

xi

Fig. 2.1] Ion inlensilies of 100gilfructose, 180gilfructose, 6% ethanol and Lang and

Summerhill icewines. . . 2-48

Fig. 2.14Comparisonofconcentr.Uion determinedbydilutionmethodrelaliveto concentration determinedbydigestionmethodfor elements throughoul the mass range, (where a value of I represenls agreement). . . . 2-48 Fig. 2.1SKeyfor a box plol.. . ... 2-49 Fig. 2.16a)andb) Backscanen:d electron photographs of calcium tartrate crystals from

Schetzinga's Pinot Noir, 1997, al magnification ofX70(a)andXISO(b) . . 2·50 Fig. ].Ia tof)Normality plot (a)and boxplot(b)ofconcenuations of Co,andscatter plot

ofconcentrations of CoandAI(c~normality plot (d) and boxplot (e) of log transformed concentrations orco.andscatterplot of log uansfonned

concentralions orCaandAI(f).. . ...]·26

Fig.].2Scree plol for PeA ofelements in wine. in which faclors are plolted against !heir eigenvalues10detennine the number of faclorsneededin the analysis ]-27 Fig. ].] Plot ofPCA Component41's.ComponenlS for element concenlr<uions

inwines. . . ]·28

Fig.].4Plot ofPCA Component41's.Component8for element concenlrolions

in wines. .. ]-28

Fig.3.5 Cluster analysis. using Pearson's R correlation coefficientsandWard's c1uslering method,showing element associalion in Okanaganwines ]·29 Fig. ].6logtransformed concentrations ofSr and Rb for Niagaraand

Okanaganwines.

Fig. ].7Logtransformed concentrations of Co1'.1.Mn in Niagarnand Okanagan wines .

Fig.].8Log transformed concentrations ofMoV.I.VinNiagaraand Okanagan wine.

Fig.].9Log transformed concentrations ofSbI'S.Uin Niagaraand Okanaganwines ..

xii

... )·)0

.. ]·)0

)·)1

... ]·)1

Fig. 3.10Logtransformed concentrations of AIvs.Cd in Niagaraand

Okanaganwines . .3·32

Fig. 3.11logtransformed concentrations ofZn\'S.Bain Niagaraand

Okanagan wines . . .. 3·32

Fig. 3.12 Plot ofdiscriminant fW'ICtion (equation 3.4), grouping Okanaganand

Niagarawine. . . .]-33

Fig. 3.13 Component 4Yl".Component 5 forOkanagan.Niagara.andFrench wines . 3-33 Fig. 3.14 Component4vs.Component8for Okanagan, Niagara, and French wines. 3-34 Fig. 3.15 Log transfonned concentrations ofSrvs.Rb for Okanagan. Niagara.and

French wines . . .. 3·34

Fig.3.16Application oftilediscriminant function (equation].4) toNiagara. Okanagan

and French wines . . ]·]5

Fig.3.11Plot of discrimination functions (equations].5 and ].6)grouping wines from France. OkanaganandNiagara regions. . . . 3-35 Fig.3.18PIO( ofdiscrimination functions classifyingwinesfrom5 Okanagan

vineyards. .. . 3·36

Fig.3.19Percent RSD calculated for 10 elements used to discriminate Okanaganwinesby vineyard. Bars rqxescnt variance (RSD) in element concentrations for 6 samples from one batch of Lake Brrett Pinot Blanc. 4 vintage years of Quail's Gate Riesling(1995-1998). 5vintage years ofHousc ofRose Verdelct(1992-1996).

andall Okanagan wines inthestudy. . .]·]6

Fig. 3.20 Cluster analysisoflog transformed concentrations of AI. V. Mn. Co, Zn. Rb, Sr.

Mo, Sb.andU.in Okanagan wine. using Pearson's R correlations as distance measu~andclusteringbyWard'smethod. . .]-37 Fig. 3.21 Discriminant analysis ofwiocsfromfour vineyanfsin Niagara.. . ]·38

xiii

Fig. 3.22 Percent RSD calculated for 10 discriminating elements in Niagara wines.

Samples from Cave Spring, Joseph's. Reif, andDcSousa are wines made from different varieties of grapes fromthesamevineyard, andtheSloney Creek samples are3 Pinot Blancs from grapesgrownon different vineyards.,butprocessed atthe

same winery . . ... 3-38

Fig. 3.23 Cluster analysisoflogtransformed concentrations of AI. V. Mn. Co. Zn. Rb. Sr.

Mo. Sb, andU.in Niagara wine. using Pearson'sRcorrelations as distance

measures and clustering by Ward's method. .. 3-39

Fig. 3.24 Concentration ofSr in wines (ppb)V$.concentral.ion ofSr in vineyard soils (ppm)forOkanaganand Niagara winesandvineyards. . . 3-40 Fig. 3.25 Concentration oHi inwines (ppb)v.r.concentration ofTi in vineyard soils

(~.)forOkanaganand Niagara winesandvineyards. . . .... 3-40 Fig. 3.26 Con"ntration ofDa in wines (ppb)VI.concentration ofBa in vineyard soils

(ppm)for Okanaganand Niagara winesandvineyards. . . 3-41 Fig. 4.1 Plot of ionic radius (inpm)"$.ionic charge for commonty occurring ions of

elements which fonnbondswith a high ionic character. as predicted by electronegativity. Ionic potentials wtJich areusedas boundaries to define element solubility. are labelled as O.03pm·1and O.12pm·'. Ionic radii are for a coordination number of6, exceptBefor which a radius for a coordination number of 4 was used. Elements which commonly exist atmorethan one oxidation stateare included for each existing ionic charge. All radii were taken from Krauskopf and Bird. 1995;boWldary definitions are from UKESCC. 2000.. . 4-12 Fig. 5.1Mapofthc "ineyardsand geology of theOkanaganValley (adapted from

Tempelman-KJuit. 1989).. . 5-19

Fig. 5.2 Map of tile vineyards and geology of the Niagara Peninsula (adaptedfrom

Haynes, 2000). . . .. 5-20

Fig. 5.3 Cluster analysis ofBaconcentrations in Okanagan vineyard soils.wheresoils are labelled by the subregion ofthe vineyard. . . ... 5-21 Fig. 5.4 Concentrations ofCr in Okanagan vineyard soils (vineyards are arranged from

south to north). . . 5-22

xiv

Fig. 5.5 Concentrations ofNi in Okanagan vineyard soils (vineyards are arranged from

south tonorth). . . 5~22

Fig.5.6 COfIOeJltraLions ofCuinOkanagan vineyard soils (vineyards are arranged from

southtonorth). . . 5-23

Fig. 5.7 Concentrations (in ppb) ofU in Okanagan wine. Vineyards are arranged from north to south,andwhere morethanone sample was taken,themoSI recenl

vintage year of!hewine sampled was ploned. . 5~23

Fig. 5.8a)andb) Box plotsofMoandNi corKXntraLions(ppb)in Okanagan wine.

showing McKenzie's wines 10beextreme high outliers. . 5-24 Fig. 5.9a) b)and c) Box plOIS

orzo,

Cd. and Cu concentrations (ppb) in wines. showing

McKenzie's wines relative to other Okanagan wines. . . 5·24

Lilt

or

Abbrniatioas . .dSymbols Used Acid mine water.

Atomic mass unilS . British Columbia . British ColumbiaWineInstitute . Chemical Rubber Company.

CounI5persecond ... _...•.•.•

Denver FederalCen~. EI~lricaJConductivity..

F-statistic to evaluatevariancebetween twogroups gnm .

High-efficic:ncy particulate air.

Inductively Coupled Plasma Mass Spectrometer.

Less than detection.

Limit of detection.

Limit ofquantization . litres ...•

Mdres.

milli Siemens . Millimetres.

Minute.

. ... AMW

. B.C.

. BCWT

CRC

. cps

. OFC

E.C.

... F . ... g .. HEPA .ICP·MS

. lO

. LO

LOQ

• •• 1

. mS

. .. min

Mol per litre.

Most probable vaJue . Partsperbillion . Parts per million.

Pearson's correlation coefficient.

Picometres .

Principal Component Analysis.

Probability value ..

Radio frequency . Recommended value .

Relative Standard Deviation ...•

Scanning electron microscope . Standard Deviation.

T-statistic evaluate differences between two groups . United States GeologicalSurv~.

United Kingdom Earth Science Courseware Consonium Vintner's Quality Alliance ..

Volts . Watts ..

Weightperccnt X-ray Fluorcsccnce..

xvii

.M

. MPV

. ppb

.. ppm .R . pm . PeA . .. P

. RF

.. RV . RSD

. SEM

.. SD ... T . USGS . ... UKESCC ... VQA .. V

. W

. wto/.

.. XRf

1.1FilleerPrilItiIt&wilta

Fingerprinting wines 10showregion oforiginhas beenexaminedforthepurpose of verifying authenticity.andis of interest inCanada dueto therecent growthand increased regulation of the wine industry.Afingerprint is an identifying pattern.andin this study refmtoapattern intraceelement concenuations which identifiesawine.a vineyard. orawine region.Traceelement fingerprintsaredeciphered in wineby the determination of multiple element concentrations followed by statistical analysis ofthe concentrations. Studies of fingerprinting wines from othergrapegrowing regions have usedtrace element concentrations (Baxterel aJ.•1997; Danzerelal.• 1999).isotoperatios (Hom^elal.• 1993:DayelaI.,1994; Eschnauer el01.• 1994).and organic compounds (MoretelaI.,1994).BaxterelaJ.(1997)analysed 48traceelements in wineandwere able to discriminate between English and Spanish wines with 95% correct classification, and between winesfrom3 regions within Spain unequivocally. Individual vineyards in the Okanagan valley were identified with 100% COfTttt c1assiflCalion using trace element composition (GreenoughelaJ.•1(97). Wines from six differentGennanwine producing regions were analysed for inorganic and organic constituents.andstatistical analysis showed five of the regions couldbefingerprinted. with 90% correct classification (Danzer elal.•1999). These preliminary studies indicate thattraceelement concentrations show good potential for fingerprinting wine.

Bedrock composition. soil chemistryandviticultural practice arc: thoughttohave a strong influence ontraceelement composition of wine. Bedrock is weathered to form soil.

andelements are tllken up fromthesoil bygrapeplants from which wine is produced.

I-I

Wine processing can also enrich or deplete some element concenlrntions in wine.

However.theprovenability lO discriminate winesbyregion oforigin suggests that regional environmental faclorsandvitkullur.ll practice have a controlling influence on element concentrations.1beaddition or depletion of some elements in wine differs with wine processing and winemaking technologies (MuryaniandPapp, 1997: Eschnauer.

1982). forexarnpletheuse of5lainless sleelstoragetankshavebeenshov,n to increase Cr andNi concenlration in wines (Eschnauer. 1982). Wine processing methodsand conditions differ with wineries and wines. therefore it is expected that element concentrations which arc strOngly influencedbyviticulture or processing will have greater within region variabilitythanbetw«n regionvariation. Wine processing practices can change from yeartoyear. so using elements which have conccnlrationsthaiare strongly affected by processing will create an unstable fingerprint. especially if it is tobeapplied to wines outsidethesample populatlon. For fingerprinting a specific wine. elements which arc influenttdbyprocessing are useful unless processing practices change significantly between vintage year.>.

1.2Major wille rrgiomi.C•••da

1beNiagara Peninsulaand theOkanagan Valley are consideredtobecool climate grape growing areas, due to their nonherly latitude compared to other grape growing regions.andtheir cold winters.TheOkanagan Valley spans 160lon, andmany ofthe vineyards arelocatedonthesloped sides ofthevalley on well drained soils of glacialand post glacial origin. Climate is governedbytheregion's location inthelee of the Coast Mountain range. The south end ofthevalley is Canada's only classified desen. receiving lessthan10 cm of precipitation per year, whereasthenonh end is slightly less arid.

I·'

receiving less than 26 cm precipitationperyear (BCW!' 2000). Allthevineyards inthe Okanaganare irrigated. with more walCr being required inthearid southendofthevalley (BCWl. 2000). More detailed discussion ofthegco'ogy and c1imale of the two regions are given in Chapler 5. Grape production in the region has rapidly expanded from 2500 acres of vineyard land in 1995 10 4200 acres in 1999 (BCWI. 2000).

Theclimate oftheNiagala Peninsula is governedbyitsproximityto Lake Ontario andLake Erie. astheselarge bodies ofwalCr storeandrelease heat. moderating the temperature of the region from cold winter air masses (Haynes. 2000).lbetopography of the 3lU is flat compared totheOkanagan. and soilsaRalso of glacial origin. but are poorly drained due 10 a subsoil accumulation of clay (ChesworthandEvans. 1982).The majordilTerencc:s in vilicultural practicebetween thetwo grape growing regions are thaI few of the vineyards in the Niagara region are irrigaled. bul many have been re-graded and hadsub-drnins added 10 improve drainage (Haynes. 2000).

1.3 Grape grow-i_C inCanad.

Grape growers have become increasinglyaWaRoflhe imponance of which grope variety grows where. creating an inlCrCSl in defining wine subregions. or appellations (Wilson. 1999). Grapevariety hasa defining influence on wine flavour. and hundreds of grape varieties exiSI. MoSI ofthe wine grape varielies produced today are members oflhe single speciesVilis vinife.ra.theonly vine which originaled from Europe (Robinson.

1996). Vilis vinife.raspecies are damagedbywinler temperatures reaching below -200C (Haynes. 2000). sothereis a risk 10 growingvinife.ravarieties in Canada's cold climate.

North American vine species. of which there are several. are better suiled to Ihe cool Canadian climate.but many. particularlyVilis /abrusca.produce wines with a marked

\.)

musky flavour. making them less populartoconsumersthanwines made from the European grapes (Robinson.1996).Hybrids are vinesbredfrommore than onespecies.

andvinesbredfromtwoorItlOf"eNorth American species are termed American hybrids.

whereasvinesbredfromVitis vinifera andAmericanspecies,are termed French hybrids.

Hybrids havebeenproduced both to suit climatic conditions ofgrapegrowing regions.

and to combat mildew and pests.TheAmerican vines are resistant to phylloxera. a louse which attacks vinerootsandhas beentermed''the great vineyard scourge-. as it killed off many ofthevineyards in Europe inthe1860's. MostVitis viniftravines are now grafted onto the roots of aphylloxeraresistant American species. or onto specially bred rootstocks (Robinson. 19(6). Both the Niagara and Okanagan regions went through a

"rebinh" inthelate 1980'5andearly 1990'5 when most oftheNonh American vine species wen: pulledoutand vineyards were replanted withVilis viniferagrapes (Schreiner. 1994; Haynes.1998).

1.4 Wine productioDi.Canada

In Canada. most grapes areharvestedin September.withtheexception of grapes for lateharvestwinesandicewines. whtch areharvestedas late astheend ofNovembeT.

Grapes are stemmed immediately after picking. to removethestem. leaves. and grape stalks. and crushed. which is done either by pressing the grape against a perforated wall or passingthefruit through a set of rollers. Sulfite compounds are sometimes added to grapesimmediately after harvesl.toprevent microbial contaminatton before cNShing (Jackson. 1989). Maceration isthebreakdown ofgrapesolidsbycrushing. allowing cxtraction of compounds from the seedsandskin into the juice. Inredwine production.

the macerate (crushed grapes. including seeds and skin) is fermented. but in white wine

'-4

production.the skinsandseeds are usually removed immediately by pressing.and the juice is fermented (Jackson.1989). To produce rose or blush wines. fermentation ofme macerate is muchsOOner-thanfor redwines.Maceration caninvolveashanexposureto hightemperalure. andthen sulfur dioxide is usually added to the juice 10 prevent microbial contamination. Sulfur dioxide prevents microbialgrowthwhile not affecting most wine yeasts(Jackson..1989). Trace elements whichadsorb10 thegrapeskinare expected 10be moreconcenltaled inredwines.Traceelement concentrationswereused to discriminate red and white wines (Greenoughel aJ.•1996); and to discriminate red. rose and white wines with100';' accumcy (Baxterel aJ.• 1997).

Oejuicing is acrom.plished first by allowing the juice torunoutfiomthe macerate under its own weight. fotloW¢dby pressing. where pressure. spread out over a large surface area, is applied10thecrushedgrapes (Jnckson. 1989). Fining. which is the removal ofsuspendedsolids.canbeaccomplished by adding the adsorbanl clay. bentonite;

by adding diatomaceous eanh. or by filtration.Theuse ofbenlOnite as a fining agent causes enrichment in rare earth elements in wine (Jakubowskiel al.• 1999).

Sugar content ofjuice is measuredastotal soluble solids (in °Brix). and detennines the capacity of the juice to suppon alcohol production. In cool climate grape pnxlucing areas such as Canada. cane sugar can beadded ncartbe end of the fermentationprocessif the juice °Brix is insufficient to generate the desired alcohol content (Jackson. 1989).

Yeastcause fennentation10 produce alcohol. where glucose and fructose are metabolised producing ethanolas one oftheproducts..WineisfermenlC'dandstored in largetanks or vats. usually made ofoak, stainless steel. or fibreglass. Owing stOfage. crystals can form from tartrate and oxalate. and precipitate out Ca and other elements (Jackson. 1989).

1-'

Wines are usually bottledand corked in the spring following grape harvest, so wine processing takes about 8 months.

1.5 WilleLabcw.e

Fingerprinting winebyits trace element concentrations has applications for detecting fraudulent wines. Wines in Canada are labelled by grape variety, geogrnphical region,andvintage year. which istheyear in whichthewinegrapes\Ir'el'Charvested. Wine production in British Columbia (B.C.) isgovernedbytheBritish Columbia Wine Institute (BCWI). which enforces regulations on winemakingandlabelling.andbytheVintner's Quality Alliance (VQA). which also sets standards forthewine industry. but to which membership is currently optional. Membership intheVQA ofOntario became mandatory on June 29. 2000,buttheOntario VQA is curmltly separate fromtheBritish Columbia VQA (Wine Council ofOntario.2001).TheOntarioandBritish Columbia VQA are inthe process ofjoining to fonn VQA Canada. to make industry standards the same for both regions.andas a consequence labelling laws of Canadian wine are undergoing change in both

=

Atthetime ofsampling.therewere slight variationsbetweenlabellingandwine producing standards inthetworegions. buttheuse of geographicandvarietal designations are similar. Varietal wines are made fromVilis viniferagropes or approved Vilis viniferahybrids.whereat Ieast8S% ofthegrapesused are of the variety on the label. Geographical designation requiresthatwinesbemade fromgrapesexclusively from a given region. Wines which are labelled with a vineyard designation mustbemade from gropes grown only on that vineyard. Wines which are labelled "Estate Bottled" mustbe

\·6

made from100"1. grapes owned or controlledbythe winery in a viticultural area (BCWI.

2000).

1.6Objectins .ad ••tliae or the study

Theprimarypurpose ofthestudy wastodetennine if it was possible10 diffem.tiate thetwowine.giowing regions ofCanadabylface clement concentration.and 10detcnnine which clements are importanttofingerprinting wine. Fingerprinting of subregions and individual vineyards was also examined within each region. and the source ofthefingerprint clements in wine was studied by analysingthesoils and examining the geology ofthe tv.-oregions.Theability10distinguish wines fromthesame vineyard within a region.and tofingerprint wines from which several vintage years were sampled. was also examined. Samples were taken totryto explain variability in clement composition of wine caused by grape variety. regional environmental effccts. differences in viticultureand

~ing.

A samplesetof95 Canadian wines. 59 fromtheOkanagan Valley and 36 fromthe Niagara Peninsula. were analysed by inductively coupled plasma mass spccuometry (ICP.

MS) for 35 elements. Wines taken fOf"thestudy were made fromgrapesgrown on a single plot of land.anda soil sample ....-as taken from the vineyard ploL To reduce the effects of varietal differences. wines made ftom the mosl popular variclies ofVilisviniftrugrapes were chosen preferentially, b«auseIhesewere the most frequently available inbothwine producing regions.Vilisvinifcragrape varietks which ...ere sampled were Riesling (29 samples. 2 of which were icewines. and 2 of which were lateharvest).Chardonnay (16 samples). Pinat Blanc (\2 samples). Gewumraminer (9 samples). Pi not Noir (7 samples).

and Pinal Oris(2samples). as well as one wine each fromthevarieties Merlot. Muscat.

1-7

Rotberger. Sauvignon Blanc. Syrah. Traminer. and Viognier. Also sampled were the varieties Baeo Noir(3 samples)., Vidal (2 samples). Marechal Foch (Isample).and Okanagan Riesling (I sample). which are French hybrids. Oftilewines sampled. only 13 of tile95 werered.beingfromthegrapevarieties Baco Noir. Marechal Foch. Merlol. Pinot Noir. and Syrah. Rotbergergrapeshave a pale neshandadaril.skin.andwine made from these grapes is fermented ontheskin. as are red wines, givingthewine a rose colour. The ability to discriminate winevarietyand colour usingtraceelement composilion was also e:o;amined.

From withintheOkanagan region. samples from 4 or 5 vintagey~of the same wine were taken from 4 wineries. for the purpose of fingerprinting individual winesand vineyard plots. Samples fromtheupper. middleandlowerpanof atankof wine.andfrom 3 differenttankscontainingIhcsame wine..."CTesampledtodeterminethevariability within a batch of ....;ne. Two icewineswerealso analysed.andtheeffects of icewine production on trace element composition were examined.

From the Ontario region. wines made from 3 to 5 grape varieties. from grapes grown onthesame vineyard. were sampled to fingerprint individual vineyards. Three wines processed atthesame winery butmadefromgrapesgrownon different vineyards were sampled. to compare variability with wines whicharc:processed at different wineries.

The variability of element concentration in wine samples which were made fromgrapes grownonthesame vineyards, but processed intwodifferent wineries. were also

1-8

2.1WI.e . . .pli. .

AJI samples were taken in theOkanaganValley and the Niagara Peninsula in April·

May. 1999.thentransportedtoMemorial University ofNewfomdland.Themajority of the samples""~rctaken fromcodedbottles of wine. excepl for16ofme samples which were takenfromprocessing tanks at wineries. Corks were removed from bottled wines and winewas poured into labelled100ml Nalgene bottles until completely full. The bottles werepreviously cleaned bysoakingovernight in2 M HNO).then rinsed with Nanopure \Wteranddriedina high-cfficiency particulate air (HEPA)-fiItered air cabinet.

whichfiltersair toreducecontamination from panicle deposition. For me 16 samples taken fromtanks. thewinewaseitherpouredoff through a spout on thetankor sampled with a pipette into a 100 ml boule.Thesample bottles were filled tothetop and then capped to eliminate air bubblesandminimizetheeffects of oxidation during storage.The samplesWttctransportedto Memorial University of Newfoundland.. and kept in cold storage (4^C1C) before analysis.Theeffects of storage on winewasexamined byBaxterel al.(1997)andwas shown 10 have no effect. although the amount oftime from beginning to the end of the study was unspecified. Wine samples in this study were stored for three to five months before analysis,andrelative standard devialion (RSD) was found to be similar for samples analysed on~consecutive days as~samples analysed several week! apart(Fig. 2.1).

,-,

2.2Wi. . .alysis

Wines were analysed for 36tr.lceelcments by ICP-MS (Li. Be. B. Mg. AI. P. S.

CI. Ca.Ti.V. Mil. Fe. Co. Ni. Cu.Zn.As.Or. Sc. Rb. Sr. Mo. Ag.Cd.Sb.I.Cs.Sa.La..

CeoT1.Pb. Bi. Th. andU).Samples were analysed in duplicate or triplicate to monitor reproducibility ofthc anaIyticaJ method.

2.2.1R~tsalld so..tioIu

Solutions were prepared using distilled HNO_{J •}Equal volumes of Fisher Reagent Grade ACS concenlIated acidandNanopure type Igradewater (17 MOem) were mixed and distilled in a su!HxHling quart!: still (QuartzandSilic:e. France). andthedistilled 8 M acid\\13Scollected in a Nalgene 201 polyethylene carboy (Longerich. 1993). Ethanol was Anhydrous Ethyl Alcohol from CommerciaJ Alcohols Inc. (Brampton. Ontano).

2.2.2 Sa_pie prtpanltM.

All sample Nbcs and standard bottles were cleaned prior to use by soaking overnight in 2 M HNO).lhen rinsing with Nanopure water and drying in3HEPA~filtered air cabinet. WiM samples~rediluted by aceunuely~ighingapproximately 5 g of wine and 5 g of 0.2 M HNO) into 16 mm by 101 mm polypropylene tubes (Sarstedt. Gennany).

This method of sample preparation. as discussed by Baxterelal. (1997). gives the advantage ofdiluting the amount of ethanol from 10-120/. in the wine to 5-60/. in the sample. and thereby diminishes matrix effects associated with ethanol. AU of the elements in gaseous state inthespray chambn are transponed to the plasma. whereas only 1·2% of sample in liquid state reaches the plasma. Organic solvents often have a higher volatility thanwater and therefore in mixed aqucous-organic solutions. more of the sample solution

2-2

reachestheplasma. causing plasma instability (Montaserelaf..1998). By reducing the amOWlt ofethanol to 5-6% inthesample. lhe plasma loading is reduced causing a more stableplasma (BoomandBrowner. 1982).The1:1dilution maintainslTM:ISttraceelement concrntrationsabovedetection limits oftheICP·MS. andthe0.1M HNOJsamplematrix maintains element stability in solution.

1.1.3 C.tibratioa ltaltdards udrdern«lII.teriab

Four external calibrationstandardswereusedfor the wine analysis. Standards were madeupin 250 ml Nalgene bottles from laboratory stock solutions which were prepared using SPEX Plasma·Gradc slandard kit powders. Standard solutions were matrix matchedtothe wine samplesbydiluting with 0.1 MHNOIandaddingethanoltoa final solution of6%w/w.Element concentrations inthe standards aregiven in Table2.1.

Water referencematerials T.123, T·127. T·I29. T·135(United States Geological Survey(USGS» and Acid Mine WaleI' (AMW·3: Denver Federal Cemre (DFC).

Colorado) were matrix matchedtothe wine solutionsbydiluting 10 ml reference material with 84ml O.ljIIHNOl •and6ml ethanol.10give afinalsolution of6%ethanol.These refe~materials were then analysed asunknownstomonitortheaccuracyandpcecision of the analyses.

1.1.4 lastra_eat.tio.

Theinductively coupled plasma mass spectrometer (ICP-MS) was a Hewlett Packard 4500 Series ICp·MS which uses a quadrupole asthemass analyser. an Ar plasma source and a concentric ncbuliser. Samples are nebulised into a Scott double pass spray chambet". where larger droplets (>10 Ilm) are deposited onthewalls ofthespray chamber

2·3

then fall into a drain.and the finer droplets. as well as the molecules in gas phase. are transported to the plasmaby thecarrier gas (Monwerel aJ.•1998).The instrument Iabon.tory is suppliedwithtwoair conditioners inorder10 minimise insttument nucruations. A Neslab CFf-15 Refrigerated Recirculator cools deionised water nowing 10 thePeltier cooling device. which in tum cools the spray chamber to 2°C. Water from the cooler is alsopassedthroughthecopper tubing ofthe load coils. and through cooling water lines tothesample interface.theturbo molecular pump at the ion lens chamber.and theradio frequency(Rf) amplifter. to prevml overlteating (HP 4500 ChemStation Operator's Manual. 1997).

1.2..5 Openli_.conditions

lnc: tuning parameters for the instrument are given in Table 2.2.Theinstrument wastuned for maximum sensitivityand uranium oxide ion formation of less than 2%.

according to HP 4500 ChemStation Operator's Manual Ch. 2. A luning solulion of 10 ppb Co. Y. Rh. Cs. Tm.and U was made up to 0.1 M HNOJand6% ethanoland used to optimise sensitivity throughoutthemass range.Optimalopernt.ing conditions. particularly the ion lens settings <Longerich etaJ.•1985).art'different for lowand high mass elements.

so tuning for the entire mass range compromisesthe sensitivity (count rate per unit concentration).

The instrument was operated at a radio frequency (RF) power of 1500 W (increased from 1200 W usuallyusedfor solution analysis) 10 accommodatethe dilute ethanol (5-6%) content of the samples (Longerich. 1989). Because of the increased plasma loading caused by the ethanol in the samples. the RF powerWQSraised to ensureQ stable plasma. A high RF power increases plasma tolernnce to organic solventsand creates

,-4

a hotter plasma with higher ionisation (Boom and Browner. 1982). Plasmatempera~.

which is decreased by the introduction of volalile solvents intothespray chamber. can be increased by I) increasing RF power (Lichte. 1987).2) decreasingArgas flow (carrier.

auxiliary.andnebulisergas). and3) decreasing sample uptake.Theplasma gas coolsthe:

outer ton:h tube. 50 because ofthc increased torch temperalwe resultingfromthehigh RF power. a high plasma gas flow·rale (14 Vmin) wasusedto coolthetorchandensure plasma stability.

Once sensilivity was oplimised. the condilions for low oxide ion fonnalion.

without loss of sensilivity.were foundbyaspiratingtheluning solutionandmeasuringthe ion inlensilies for !lIU'andits oxide :mUltO'.Theoxide ratios (25412]8amu)were approximalely I,..•. and didnotexceedl-A..Thefonnation ofoxides decrease with an increase in plasma temperature. as more bondsbreak(bondsbreakwhen heat energy is greater!hanbond energy). and equilibrium shifts from MO· 10 M'+O. reducing polyatomic species fonnation in the plasma..Theoperating conditions determined for opIimal sensilivity with low oxide ion fonnalion are given in Table 2.2.

Nebulisergasflow has a strong effecl ontheion signal inlenSity (Longerich.

1989). The effttl of nebuliser gas flow on signal inlensity. oxide formation. argide fonnationanddouble ion formation was examined for a number ofelements. The data acquisition software was programmed 10 count each ofthc desired ion intensities for 10s- withthenebulisergasflow-f3.tesetat 0.5 Vmin.Thenebulisergas flow-rate was then increasedby0.1 Vminand theion intensitieswere acquired again. This was repeated over therange of nebulisergasflow-f3.les from 0.50 to 1.50 Umin.andion signal intensitywas then plotted as a function ofncbuliscrgasflow-ratc (Figs. 2.2 to 2.5). Thoriume)~')is a good indicator of polyatomic ion fonnation because it formsthestrongest oxide bond

2-5

(204 KcaUmol) of allthe elements except calbon (CRC Handbook of Chemistry and Physics. 1983). and therefore provides a "worse case scenario" for oxide formation for a given set of operating parameters. Fig. 2.2 shows optimal ion intensity. with low oxide ion interference. otturs at a nebu1iser gas flow-rate of 1.0 lImin. The background signal.

monitored at 227 amu. wasst1bJethroughout the range of nebulisergasf10W-r.ltes.

Although Co fonns much ,-,'eaker oxide bondsthanTh (88 Kcallmol. CRe Handbook of Chemistry and Physics., 1983). ion intensity and oxide formation as a function of nebuliser gas flow-rate were examined in Fig. 2.3 to show a nebuliser gas flow-rate of 1.0 Vmin gave high sensitivityandlow oxide ion formation for a low mass e1cmenL

The:effect of nebuliser gas flow-rate on the sensitivity of IOOCe was examined. as well as oxide formation (I-ccl*(}·). and doubly ch:u'gedjon formation(~c")(Fig. 2.4).

The signal intensity was found tobeoptimal at a nebulisergasflow-rate of 1.1 Vmin. with oxide formation de1::reasingwithdecreasing gas flow. and a high sensitivity and low oxide formation occur at 1.0 Vmin gas flow rate. Doubly charged ion formation. which usually decreases with increasing gas flow, was found to be low throughouttherange of nebuliser gas flows.Thenebulisergas flow-ratc was therefore set to 1.0 Vmin as a comprimise between high sensitivity and low oxide fonoation.

Because thc hotter plasma associated with running at an RF power of 1500 W causes a higher tcmpcrat.ure torch box.thewatercooler was found tobe inadequate at keepingthecooling water at 2O"C. Temperature drift inthe water coolingtheloading coils ofme torch can ClUte instability inthe plasma conditions.Thecooling wateT temperaturewasset to 25°C. to keep the water temperature stableand thetorch conditions constant.

, ..

Because many ofthelow mass elements were importanttothis study.andhave higher backgroundsaswellas more interferences than do high mass elements. the nebuliser gas flow-rote wasfurtherexamined. wherethelow mass elements were optimisedin termsof maximwn siana! intensity (Fig. 2.5). Optimal signal intensity was foundtooccurat1.1Vminnebulisergas flow-rate. Whilethesignalintensities for "Mn anduCu decreased fOf"gasflow-rates greaterthan1.1 Vmin. the: signal at 42 amu remains above 100.000 eps. This is due to an increase in argide fonnation at highergasflow-rates.

and thesignal at42amu becomes enhancedbythe presence of.l(JAr'H~.Thesignal at 43amu is alsoenhanced.though toalesserextent.by thetail of1:!<:16

0:randthe fonnatioo of"ArH'Hand'lIAr]Hj •Argideformation. along with all polyalomic ion formation. is minimisedbyloweringthegasflow-rate. but is accompaniedby a lossof sensitivity. Sening the nebuliser gas flow-rote to1.0Ilmin compromises a high sensitivity with [ow argide fonnation.

2.2.6Hadq;ro. .d ioai.tn.sitia

The formation of poIyatomic argides. hydrides. oxides. nitrides and carbides inthe plasma causes high background intensities. especially in the low mass range.Thehigh ion intensities for the 60/. ethanol blank. as compared tothe0.2 M HNOJandHpblank solutions. rfiulted in high detection limits for several ofthelow mass elements duetothe:

foonation of polyatomic species.Thespectrum forthelower mass range of a 6% ethanol blank is compared tothespectra. of 0.2 M HNOjandHlO in Fig. 2.6.Thelow mass spectra of2 wine samples. Irvine ChardoMayandDeSousaHaeo Noir. are plolted with the spectra ofthe60/. ethanol blank. in Fig. 2.7. to show ion intensities in wine compared tothosein the calibration blank (background intensities). For ions where the concentration

2-1

of the analyte in the sample is below or close 10thebackground intensity, which is high due 10 high backgroundsfrompolyatomic ion formation, quantitalive analysis isnot possible. Elements whichwerebelowdetection.limitinmost ....ineswere~i.due tothe OCCWTet'lCe of 1'="'0 at 28 amll, .uSc. dueto theoccurrence of'~'~'~'Hat 45 amll, andnCrandIlCr. because ofthefonnation of.l(lAr^I,=and4OArl~IH.Thepolyalomic species 40ArlJC is also present at 53arnu.but its abundance is relatively low compared to thatof 40Ar^I1CH.

In addition.analyticaldata for BandS were discarded.Theelement."'8.WilS

below detection inmostwinesandall reference materials. duetohigh backgrounds.The element. B. has a low mass and a high ionization potential so quantization by ICP-MS is eltpected tobepoor. The use of boric acid in sample preparation for rock samples analysedbytheHP 4500 Series ICP·MS mayalsocause memory effects for this element.

Backgroundswerealso highfor~.causing high detection limits and poor~ision.

Because metabisulfite is used to prevent bacterial growth in wine processing and in storage equipment. high variability in S concentrations may occur between wines fromthe same vineyardanddiffe~tbatchesofthesame wine.andtherefore S isnote.'(pected to be a useful element for discriminating wineby~iOQ_

2.2.' Choi«

or

^t5otopes

For elements wheretwoor lTlOfe isotopes eltist. analyte ions can be chosen by examiningthespectra for masses with high anaJyte isotope abundanceandlow interferencesandbackgrounds.Theisotope nCI was measured duetothehigh background on nCI caused by 36ArIH. Both4:ZCaand^4JCa were measured. but the detenninationof~JCawas used inthedata analysis., because despitethehigher isotopic

2-'

abundanceof'~theOCCIIlTenCeor'llMH at42 ainUcauses a high background.The olher isotopes orCawere not used for the dctennination.9ue to the presence of Ar at 40ainU.IIC(~'~at44amuandtheisobaric interferences withTiat46and48amu.

Threeisotopes of Feweremcasumi.at54. 56. and 57 amu. De1mnination oftheisotope

"Fewasusedas a high background occwttdat56 amufrom-Ar1'O. a high background occurred at 54 amu due 00 "'Ar1-N.andCr causes an isobaric interference at 54 amu.1be two isotopes"Seand 11Se wcre measured. wilh high backgrounds occurring due to 'llA~ArIHand "'lAr1IH_{l •}andinterferences occurring from "'Ar' CI and ·'BrIH.The detennination ofl;!Sc wasused forthedataanalysisalthough thedetection limits ofthe two isotopes are similar. Iktennined concentra1ion oflhesctwoSeisotopes sho\lo'ed good agreement. which is evidence for good backgroundandinterference correclion.

2.2.8An.log c.libraltoa

1beHewlett Packard 4500 Series ICP·MShas twomodes of detection: a pulse counting mode. which measures ions in countspersecond(cps).andananalog mode.

which measures a current signal. and is cross eaJibrated withthepulse counting modeto produce an equivalent cps.Thepulse countingmodeis used for low ion intensities. up to about1.000.000 cps. and the analog mode. which overlaps the upper range of the pulse counting mode. canbe usedfor signals above10.000 cps.. but is betternotused until required (at 1.000.000 cps). as pulse countinghasless noisc. Calibration oftheanalog cUlTCntlO equivalent cps is performed once a~kaccording 00theprocedure inthe HP4S00 ChcmStation ()pcrator's manual. using tuning solutions made up in0.1M HNOI

and 6% ethanol. to givc signals with low RSD in both modes.

2-9

:U.9 Dal• •~lIililion

TheelementsIOJRhandInRe were used as intemaJ standards becausetheyare not present in significant quantities in wine.Aninternalstandardcontaining 2 ppmRhandRe in 0.2 M HNOJis introduced tothesample lUbebya y-eonnec:tor.togive a sample solution containing approximately 0.1 ppm internal standard. Both the sample uptake and theintemaJ standard uptake are controlledbya peristaltil;: pump. and the sample solution reachesthenebuliser at arateof 0.4 ml sample/minand0.02 mt internal standatdlmin.The:

use oftwointemalstandards allows theinterpolation or extrapolation of a matrix correction throughout the mass range (Longerich. 1990).

A wash cycle with 0.5 M HNOjfor 180 s was perfonned between each analysis.

Two flush sampleswereplaced atthebeginning of each run in order to allow the uptake of internalstandardsolutiontoreachthey<onnector.

Samples were run insetsof 14, in whichthettare four calibrationstandards (solutions containingknownamounts of specific elements (HP 4500 Chemstalion Operator's Manual». a flush sample (a solution used to rinse out residual analytes caused byhigh concentrations inthesamples). a calibration blank (a solution used to detennine background concentrations forthecalibration solutions; a reagent blankisa blank fOC"the sample solutions. but was not used in this study because samples and calibration solutions were matrix matched). six unknown wine samples and two reference materials. Two wine samiHes. Irvine ChardonnayandInnislcillin Chatdonnay.~reused as in·house reference malerials and one of these samples was analysed inevftyrun.to monitor long teon reproducibility. Cycles of 14 lUbes (data acquisition for I cycle takes 170 min) ....'ere used to provide frequent re-ealibration and drift correction. Because of the increased volatility ofthe6% ethanol samples compared to aqueous solutions, evaporation is more of a

concern as it alters c:onc:entration. To reduce evaporation effects.NItSwere no longerthan 3 cyclesandtherubes ....~kept covettd until irnmtdiately befOC'etherun commenced.

Calibration standards. which are II$UaIlymeasuredrepeaeedly from 500 ml bol:t1es throughout many nms. were poured into rubes for analysis. to ensure that evaporation effects arethesame for samplesandstandards. Using larger vessels for repeated measurementsandNItSmay allow evaporation ofethanol over time. alteringthe concentrntionofthe standaldsandcausing a difference in matrilt bet"...eenthesamplesand standards.

Thewine standardsandsamples wen: analysed by ICP-MS. using the operating parameters given in Table 2.2. The ion intensities are recorded as cps (countspersecond).

andtheunknowns arethencalculated as concentrations in pansperbillion(ppb) accordingtothe procedun: described in detail in Appendix I.whichwas wrinen as a Lotus \·2-] (Release 5) spreadsheet. Wine.wk.4.

The data was reduc:ed lirst by matrilt colTcction. then blank correction.

Interference corrections weremadefor CI on~.SIV.and77Se;Br onuSe;andCa on

"Fe. "Co.~i.and..sell.,for which ion intensities of interfering species are shown in Fig.

2.8 and 2.9. Also shown in fig. 2.9 is the ion intensity of the interference caused by Cion

J'

As. which was negligible and therefore not corrected. Sensitivity (cpslppb) of analytes throughout the mass range. normalized to isotopjc abundance. is given in Fig. 2.10 (calculation for sensitivity given in Appendix A). Ion intensity (in cps). was dividedby sensitivity and by dilution factor to give concentrations (in ppb) ofanaIytes in wines.

2-11

2.3.1Dd«tio.limits

au

pm:isiH (asRSD)ulc:u..ted fortIIchelement Detection limits (DL) and limits ofquantization (LOO) for themethodare calculated for quality assurance and to anow interpretation ofthe unctttainty ofthedata.

especially for analytes with concentralioos nearOf"below the DL. The DL is defined asthe lowest concentration that can be detennined to be statistically different from a blank (Douglas. 1992). The solution detection limit was calculated as 3 timesthestandard deviation of the mean count raleofthe analyte in the calibfil.lion blank. divided by its sensitivity in ppOIcps.Thesample detection limit is calculated for each analyle of each unknown sample. by dividingtheelement detection limitbythe sample dilution factor. If the sample detection limit for an element is greaterthanthe concentration calculated for that element then the precision oftheanalysis is poor asthepercent relative standard deviation (RSD) for that concentration is greater than 33%. Confidmce intheapparent analyte concentration increases asthe anaIyte signal increases above the DL (Keith et al..

1983). Limit of quantization (LOQ). calculated from 10 times the standard deviation of the blank divided by sensitivity. gives a morc rigorous parameter for detection.TheLOQ is defined as the level above which quantitative results may be achieved with a certain degree ofconfidence (Keithet al. 19&3). Table 2.3 gives mean sample DL and LOQ's for the method. calculated as the solution DL or LOQ divided by the mean sample dilution factor.

Thereproducibility oflhe method was detenninedbyanalysing each of the wine samples in duplicate or triplicate. Precision. as RSD for each anaIyte. calculated as the mean RSD of the sampleswithconcentrations above LOO. is reported in Table 2.3.

Thedetection limits for many of the lower mass elements were high for wines measuredbythis procedure due to high backgrounds from polyatomic ion formation. but

2-12

none ofthe:wines analysedhadconcentrations below LOQ forthe:elementsLi.Be. Mg.

AI. Ca. Ti. V. Mn. Fe. Co. Ni.ell.orZn were below LOQ,

Poor precisionwasfound forthe:halogens. CI. Sr. andI.which also have high detection limits associated with them because of their high ionization polemial.andalso due high backgrounds for Cl and Sr (Section 2.2.7). High analytical backgrounds forlhese elements occur in St John's. wherelhewines were analysed. due to high atmospheric concemralions from road saltandsea spray. Ohhe halogens.. Ihasthe lowest detection limil due 10thelowbackgroundat 127 amu. which is only tonlinuum as there are no polyatomic species causing high background at litis mass. It is also monoisotopic andhas Ihe lowest ionization potential orthe halogens. although il is also Ihe lowesl crustal abundance halogen.

Thedetmion limitandRSD oftbe element P was also high. due 10 high backgroundsand low sensitivity due 10 a low ionisalion potentLaI (Douglas. 1992). Low Kmisalion polenlial as well as polyatomic background and intetfering species also cause poor precision for$e.and concentrations inmostwines are below LOQ (but above Dl).

Several detennined concentrations oflhe elements Ag and Bi were below dettttion limit.

1.3.2 Aecuracy of the met"od

The meanmeasuredconcentrations of elements inthefive watersref~

materials (USGSand DFC) are compared 10therecommended values (RV) for the reference materials. and most probable values (MPV's) compiled frompastruns onthe instrumem (HP 4500 Series at Memorial Universil)' of Newfoundland}. which are used as in-house reference values (Table 2.4). The MPV's compiled at Memorial University of Newfoundland have been found to be consistentlyand significantly differemthanRV's for

some values, which is thought 10 be due 10 RV's being compiled from a large database with high variability.Whereconcentrations compiledfrompastNI'IS~found tobe significantly differentfromrecommendedvalues.MPV's are given. and wereusedas

"most probable" values instead oftherecommended values. Because the walers reference materials were diluted 10 limes.thesample delection limits oflhese malerials are approximately 5 limeshigher-Ihanthoseofthewines.although thesolution detection limits remainthesame as forthewine samples.

Thcre is poor agreement between the MPVandthe mcasured concentration for Zn in reference T-123. becausetheMPV is belowtheLOQ.Theconcentration ofln in Ihis reference material is wellbefowtherange of concentrations of Zn in the wine samples.and the measuredvalues for Zn show good agreement with MPV's intheother reference materials, which contain higher concentrations.

None of the halogens delermined (CI. Or.andI)hadMPV's available forthe reference materials. sotheaccuracy of their dclemtination couldnotbeassessed.

CoocentnJ,tions ofl were found to be highly variable between wines. suggesting it maybe of use in discriminaling wines. In theenvironm~nt.I exists in several oxidation states. In highly oxidizing environments, I is in the fonn of10_1",which is a mobile and stable species. In itsmostreducedfoem, I", it is also mobile. but canbeoxidill:d to the volatile species Il.Wines are a reducing environmenl, as the alcohol-producing yeast use up the oxygen creating an anaerobic environment.andI is therefore expectedtoexisl in its reducedform. I".Thewine samples were dilutedto0.1 MHNO).so it is possible Ihall"

was oxidised toI~,\\/henthe samples are analysedbyICP-MS.theI~would easily convert to a gas upon nebulisation. and a signal enhancement would occur as gas is lransported fromthespray chamber 10theplasma with 100% efficieocy. whereas only 1-2% of the

solution reachestheplasma.1hc oxidation staleofl inthecalibrationSWldard isdiff~t from lhal ofthesamples.aspara:periodic acid is acidified wi!h 0.2 M HNO). so I is inan oxidizedstateas10;,andwillnotbevolatilised inthe spraychamber.1hc uneutainly in thespeciation ofl in solution alsocreatesuncertainly intheaccuracy ofthe wine analysis.

The inclusion of e1emenls in!hefingerprinl analysis is further discussed in Section2.6.The elementsP, CI. andBr. for which accuracy and precisionaresuspected10 bepoor.were left out oftheanalysis., whereas fortheelementsSe.Ag, 1andBi. concentralions in wine were examined relative10Olher e1emenlS. but werenotincluded intheregional fingerprint

2.4Iccwi.n

Icewinesaremade fromgrapeswhich are IlOlturally frozen onthevine 31-SoCor lower (VQA Icewine Faetsheet2000). Grapes are harvesled for icewines much laler than other wine grapes. often in lale November. so !hese grapes left on lhe vine have more time 10 bioaccumulate (race e1emenls. Grapes also wither and shrink when !hey are left onthe vine !his lale. which may cause element concenlrntions inthefruit to become more concentraled. Grapes areharvestedandpressedwhile frozen.and mostof thewaterinthe grape cells remains inthepress as ice. sothejuice is extremely concentratedand hasa highsugar content (Jackson. 1994). Due tothehigh concenlnllion of sugar intheicewine juice.thewine is leftwitha residual sugar concentration of greaterthan100gilafter femenlalion (which converts sugar 10 ethanol and COJ. This high sugar conlent in icewines causedthesesamplestobe poorly matrix marched to the calibration standards and blanks. and high backgroundsandinterferenccs were suspected due to the higherC content oflhcsc samples. To dClcnnine the accuracy ofthc analysis. and (he high

2-15

backgrounds caused by the sugar content. full mass spectrawerecollected forasolution of 6% ethanol in 0.1 M HNOJ(calibration blank).asolution of 6% ethanol in 0.1 M HNOJalso containing 100 gilfruc:tosc.a solution of6%ethanol inO.1 M tlNO) containingISOgilfNCtose.andtwo icewines (Lang and Sununerftill Rieslinglccwines).

Sugar in wine is actually approximatelySO%fructose (C,HI)O.) and50-10glucose (C.HI20.).but the two molecules are expected to have similar speclra due to their similar formula andstNCt~.High backgrounds in !he fruclOSe solutions.. compared to the calibration blank, are due to polyatomic species containing C. H. and 0. Fructose has a molecular weight of 180amu.and the fructose solutions have higher ion intensities at this mass than does the calibration blank. indicating that a small percentage of the fructose molecules are not alOmiscd in the plasma.Thefructose molecule (Fig. 2.11) breaks in the plasma10fonnthe:speciesCH~OH.c-oat59amu...causing a high background for Co.

and(CHOH»)CH~OHat 121amu.causing a high background forSb(Figs. 2.12 and 2.13).

Several hydrocarbon species fonn in the intennediate mass range. some of which are CH~OH-COHat60amu which causes a high background for Ni. O=COH-CHOH at 75 amu which causes a high background for As.andO=C-{CHOHh causes a high background at "Sr(Fig. 2.13).

2.5 WinediCesl!

Ten ofthe:wine sampleswerepreparedfor analysis by a digestion procedure. used byGreenough elW.,(1996 and 1997), as well as by the dilution method described above.

This study.servro two purposes:I)thecomparison of the digested sample data with the dilution method and 2) the removal of interferences caused by ethanol carbon in the

2-16

diluted wine samples. which especially effects the elements SiandCrduc: to high backgrounds fromthefonnation of COand ArC.

2.5.1Dipstioaproeect.re

From each wine sample. 20gof wine was weighed accumtely into a 30 ml Teflon digestionjar.Theflasks were placed onahotplate at 90°C andthesample volume was reducedtoa fewmls.Approximately 2·)mtof 8 M HNOJwasadded and thewines were taken almosttodryness.TheresMiualwasthendissolved in approximately 20gof 0.2 M HNO) (Greenough elal.• 1997).

2.5.2A••lysisbyICp·MS

Thewineswereanalysedbya Hewten·PackardSeries4500 ICP-MS for the elements Li. Ik. B. Mg. AI. Si. P. S. CI. Ca..Ti.Cr. Fe.MR. Co. Ni. CU.Zn. As.Br. Se.

Rb. Sr. Ag. Cd. Sb. I. Cs. Ba.La. Ceo Tt Pb. Bi. and Th. Because the ethanol was removed fromthesamples. the wines were analysed as waters with an RF power of 1200Wanda nebulisergasnow of 1.0 Vmin.Thecalibrationstandardsweremade:up in 0.2 M HNOl •andcontainedthesame elements asthoseused forthewines (Table2.1).

except for the exclusion ofTh from Standard A. as itwasusedas an intemal standard. A solution of2 ppm·'Sc. lO)Rh. '17Re and~l2"fhin 0.2 M HNO) was used as an internal standard.and all count ratedatawas corrected for matrix effects.. backgroundanddrift. as withthedatareduction procedure forthewine samples.Theonly element with a mass greater thann~was})IV.andfor this element matrix effects were linearly extrapolated from the internal standards. Linear interpolation fromtheinternal standard data. was used for elements with masses between~IScandn~.For elementswithmasses less than"s<:.

thematrix effects were linearly extrapolated. which is different fromthe datareduction of thewine samplespt'epaI'ed by thedilution method,\\otletematrix/drift correction factor wasnotextrapolated(butkeptconstant) for elements lighterthanIOJRh.Forthc:wines prepared bythc:dilution method. a Jow mass internal standard could not be used due10 highbackgrotmdsfromtheethanol in the samples.andextrapolation from103amu was determined tocauseextremecolTe'Ction factors inthelow mass range. Blanks were subtractedandknowninterl'erencc:s, which forthedigested samples were CIwithliV.

1SAs. and TlSe; Ca with "Fe. "Co, and~i;and Br with11Se.were correcled for mathematically. Sensitivityandconcentration werethencalculated usingthefour external slandards. as withthediluted wines. The disadvantages ofthedigestionmethodcompared tothedilution method are that volatile elements (especiallyCI. Dr.andSe)are lost upon evaporation of the sample. the additional reagents used for the digestion increase lhe risk of contamination.andsample preparation ismoretime conswning.1beproblem of polyatomiccarbidescausing highbackgroundsis considerably reduced inthedigestion method bytheevaporation of ethanol. but significantCconcentrations were slill present due to C in the Ar gas supply and C entrained fromtheatmosphere.Theremoval of ethanol loweredthedetection limits for CrandSiena~ingthemto be quantified.ashigh backgroundsobscuredtheseelements from being determined bythedilution method.The results fromthedigested wine showgood agreementwiththose prepared by the dilution method (Fig.2.14)forthcanaIytesMg, AI. P.Ca, Ti.V.Fe. Mn. Co. Ni. Cu. In.As.Rb.

Sr. Mo. Cd. Sb. Cs.8a,La.CeoTI.Pb.andU.