Accepted Manuscript Intraregional Classification of Wine via ICP-MS elemental Fingerprinting P.P. Coetzee, F.P. van Jaarsveld, F. Vanhaecke PII: DOI: Reference:
S0308-8146(14)00731-6 http://dx.doi.org/10.1016/j.foodchem.2014.05.027 FOCH 15808
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
24 January 2014 25 April 2014 10 May 2014
Please cite this article as: Coetzee, P.P., van Jaarsveld, F.P., Vanhaecke, F., Intraregional Classification of Wine via ICP-MS elemental Fingerprinting, Food Chemistry (2014), doi: http://dx.doi.org/10.1016/j.foodchem.2014.05.027
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1
Intraregional Classification of Wine via ICP-MS elemental
2
Fingerprinting.
3 P.P. Coetzeea,*, F.P. van Jaarsveld,b F. Vanhaeckec
4 5 6 7 8 9 10
a
Department of Chemistry, University of Johannesburg, Johannesburg, Box 524, Johannesburg 2006, South
Africa. E-mail: [email protected] b
ARC
Infruitec-Nietvoorbij,
Private
Bag
X5026,
Stellenbosch
7599,
South
Africa.
E-mail:
[email protected] c
Department of Analytical Chemistry, Krijgslaan 281-S12, Ghent University, B-9000 Ghent, Belgium. E-mail:
[email protected]
11 12
*Corresponding author. E-mail: [email protected]; Tel: +27(0)11 5592558; Fax: +27 (0)11 5592819
13 14
Abstract
15
The feasibility of elemental fingerprinting in the classification of wines according to their
16
provenance vineyard soil was investigated in the relatively small geographical area of a
17
single wine district. Results for the Stellenbosch wine district (Western Cape Wine Region,
18
South Africa), comprising an area of less than 1000 km2, suggest that classification of wines
19
from different estates (120 wines from 23 estates) is indeed possible using accurate elemental
20
data and multivariate statistical analysis based on a combination of principal component
21
analysis, cluster analysis, and discriminant analysis. This is the first study to demonstrate the
22
successful classification of wines at estate level in a single wine district in South Africa. The
23
elements B, Ba, Cs, Cu, Mg, Rb, Sr, Tl, and Zn were identified as suitable indicators. White
24
and red wines were grouped in separate data sets to allow successful classification of wines.
25
Correlation between wine classification and soil type distributions in the area was observed.
26 27
Keywords: ICP-MS; multi-element analysis of wine; wine provenance; multivariate
28
statistical analysis 1
29
Abbreviated running title: Intraregional Wine Fingerprinting.
30 31
1. Introduction
32 33
In recent years, much progress has been made in food authentication through fingerprinting
34
techniques (Kelly et al, 2005), in particular in terms of provenance determination. The
35
method combines chemical analysis, by any of a variety of instrumental analytical techniques
36
(primarily trace element and isotope ratio analysis), and multivariate statistical analysis
37
(Tzouros & Arvanitoyannis, 2001; Serapinas et al., 2008) of the chemical data, to obtain
38
identification and classification of an agricultural product according to geographical origin.
39
The method assumes that the chemical composition of an agricultural product, such as wine,
40
will reflect the composition of the provenance soil. In the case of wine, studies of this nature
41
are being pursued in most wine-producing countries (Martin et al., 1999; Taylor et al., 2003;
42
Gremaud et al., 2004; Thiel et al., 2004; Angus et al., 2006; Tarantilis et al., 2008; Galgano
43
et al., 2008; Gonzálvez et al., 2009; Laurie et al.; 2010; Fabani et al., 2010; Fabrina et al.,
44
2011; Di Paola-Naranjo et al., 2011). These studies focus on distinguishing wines from
45
different countries and some also attempt to classify wines from different regions in one
46
country. The extensive literature, covering the progress made in this field since the idea was
47
first explored in the 1990’s (Swartz & Hecking, 1991; Baxter et al, 1997), is reviewed
48
elsewhere (Suhaj & Korenovská, 2005; Capron, 2007; Giaccio & Vicentini, 2008;) and is not
49
discussed here.
50
In previous work (Coetzee et al., 2005; Coetzee & Vanhaecke, 2005; Vorster et al., 2010;
51
Coetzee et al., 2011) it was demonstrated that South African wines can be distinguished from
52
wines from Europe and that the interregional classification of wines within South Africa, is
53
possible using ICP-MS for multi-element and isotopic analysis (11B/10B ratio). The
2
54
correlation between the trace element composition of a wine and that of the provenance soil
55
was verified (Van der Linde et al., 2010).
56 57
In the current work, intraregional classification of wines from one region or part of a region
58
only, was investigated, in order to establish the limits and reliability of the application in the
59
relative small geographical area of a single wine district. The main objective was to ascertain
60
whether differentiation was possible at ward level or even at estate level. A section of the
61
Stellenbosch wine district, ranging from the Helderberg mountain in the south to the
62
Simonsberg mountain in the north and covering an area of about 1000 km2, was selected for
63
the study (See Figure 1). The area is characterised by a complex distribution of soil types and
64
this was taken into account in the selection of wards and estates included in this study. The
65
smaller the geographical demarcation is, the higher are the demands on the accuracy and
66
precision of the analytical data obtained by ICP-MS for successful classification. Diligent
67
application of quality control procedures to ensure reliable analytical data was therefore
68
required.
69 70
2. Materials and Methods
71 72
2.1 Wine samples
73
A total of 120 wine samples (93 red, 27 white), from 5 wards in the Stellenbosch area in the
74
Western Cape Wine Region, was collected from 23 estates and wine cellars during the first
75
quarter of 2010. In order to obtain a sufficient number of samples per estate, wines were
76
selected from the 2001 to 2010 vintages, depending on the availability of suitable samples at
77
the respective estates and cellars. The red cultivars consisted of Cabernet Sauvignon,
78
Cabernet Franc, Malbec, Merlot, Petit Verdot, Pinot Noir, Pinotage, and Shiraz. The white
3
79
cultivars were Chardonnay, Sauvignon Blanc, Chenin Blanc, Gewürztraminer, Semillon,
80
Viognier, and Weisser Riesling. All wines were made from grapes produced in identified
81
blocks pertaining to the particular estates. Table 1 lists the wards and estates/cellars plus
82
cellar codes and also gives the major soil types associated with the wards from where the
83
wines were sourced.
84
/ Insert Table 1 /
85
2.2 Soil characteristics
86
In previous work (Van der Linde et al., 2010), the link between the trace element
87
composition in a wine and that in the provenance soil was established for South African
88
wines. This is an essential precondition for the application of multi-element data and
89
multivariate statistical analysis to the classification of wines according to geographical origin.
90
In this work the complexity of soil type distributions in the Stellenbosch area was taken into
91
account in selecting wines from wards representative of different soil types.
92
The distribution of the soil types (Hartmann, 1969) in the sampling area is indicated by soil
93
type codes in Figure 1.
94
Three major soil types occur in the area where the wines were sourced.
95
These are:
96
• red kaolinic loams and clays of the granitic foothills (Code F2 and F3), for example
97
found in the Simonsberg-Stellenbosch ward on the western foothills of the
98
Simonsberg.
99
• duplex soils, mostly coarse alluvial sands and residual clays (Code D4S and D3S),
100
derived from granite and shale, for example found in the Stellenbosch-West ward.
101
• weakly developed hydromorphic soils derived from alluvial and fluvial sandy deposits
102
along rivers and streams (Code I1), for example, areas along the Eerste River in the
103
Stellenbosch West ward.
104
4
105
Small pockets of poorly developed loam shale-based soils (C2) and duplex fine sand or loam
106
top soils (DIL) are dispersed among the three major soil types contributing to the overall
107
complexity. These pockets are found in the Eerste River Valley between Lynedoch and the
108
river mouth in False Bay.
109
/ Insert Figure 1 /
110
It is clear from this picture that a ward could include any combination of three or more
111
different soil types. The approximate positions of the wards where wines were sourced are
112
indicated by ovals in Figure 1. The soil type alone is not necessarily an indication that trace
113
element differences between soil types would be sufficient to allow for successful provenance
114
determination. How this variability reflects the trace element composition of the soil is not
115
generally known. It was the aim of this work to establish whether these differences would
116
allow classification of wines according to estate.
117 118
2.3 Sample preparation
119
Volumetric equipment was soaked in 1% distilled HNO3 overnight and thoroughly rinsed
120
with Milli-Q water, before use. High purity 18 MΩ.cm water was obtained from a Milli-Q
121
purification system and was used in the preparation of all solutions. HNO3 was purified by
122
distillation in a sub-boiling quartz distillation system. High-purity ethanol was used for
123
preparing matrix-matched standards.
124 125
Wine samples were diluted 1:10 with distilled 1% HNO3 and indium was added as internal
126
standard (final concentration: 100 µg/L). The dilution reduced the ethanol concentration to
127
between 1.2 and 1.5% and residual sugar to < 0.3 g/L or 0.03% (because only wines
128
classified as “dry” were sourced), which was sufficiently low to diminish matrix effects and
129
plasma instability caused by organic matrices in the plasma during measurement.
5
130 131
The method blank was a 1.2% ethanol/1% HNO3 solution containing 100 µg/L of the In
132
internal standard. Standards were prepared in 1.2% ethanol/1% HNO3 by appropriate dilution
133
of 1000 mg/L single element Merck ICP standard stock solutions and the addition of 100
134
µg/L of the In internal standard. Two matrix-matched calibration standard series were
135
prepared to match the expected concentration ranges in wine of the 18 elements selected for
136
this study: Low Standard series: Al, B, Ba, Cu, Mn, Rb, Sr, Zn at 50, 250, 500 µg/L and Cd,
137
Co, Cs, Li, Ni, Tl, U, V at 5, 25, 50 µg/L. High Standard series: Ca, Mg at 5, 25, 50 mg/L
138 139
2.3 Quality control (QC)
140
A 100 µg/L Initial Calibration Verification (ICV) standard in 1.2% ethanol/1% HNO3 was
141
prepared from 1000 µg/L multi-element ICP standard (Merck) containing the elements: Ag,
142
Al, B, Ba, Bi, Ca, Cd, Co, Cr, Cu, Fe, Ga, In, K, Li, Mg, Mn, Na, Ni, Pb, Sr, Tl, Zn. The
143
multi-element standard already contained In, therefore no additional internal standard was
144
added.
145 146
Continuing calibration verification (CCV) standards were prepared from single element ICP
147
standards (Merck) consisting of 20 mg/L Ca, Mg for the high standard series and 250 µg/L
148
Al, B, Ba, Cu, Mn, Rb, Sr, Zn and 25 µg/L Cd, Co, Cs, Li, Ni, Tl, U, V, for the low standard
149
series. The CCV’s were measured after every 10 samples.
150 151
Duplicates of two white and two red wine samples were prepared.
152 153
Possible matrix effects were checked by running an interference check sample (ICS)
154
consisting of Ca (50 mg/L), Na (100 mg/L), Mg (250 mg/L), K (500mg/L). In addition, 6
155
spike recovery tests and serial dilutions were done on red wine and white wine samples. The
156
spiked samples were prepared at concentration levels of 20 and 100 µg/L for the elements Al,
157
Ba, Cu, and Sr and of 100 and 500 µg/L for the elements B, Mn, Rb and Zn. A serial dilution
158
check (1:10 followed by 1:3, thus 1:30 final dilution) was done on one white wine and one
159
red wine.
160 161
2.4 Instrumentation
162
Analyses were carried out with a quadrupole-based Thermo X-Series 2 inductively coupled
163
plasma-mass spectrometer (ICP-MS) equipped with nickel cones, a Peltier-cooled, low-
164
volume conical spray chamber fitted with a fixed impact bead and a high-performance glass
165
concentric nebuliser, a peristaltic sample delivery pump, and a Cetac 500 autosampler.
166
Instrument operating conditions were optimised for analysis of the diluted wine samples.
167
Mass calibration and detector cross-calibration were performed according to the instrument
168
manufacturer’s instructions, using the prescribed solutions obtained from Thermo. A
169
sensitivity check using a 10 µg/L Ba, Be, Bi, Ce, Co, In, Li, Ni, Pb, U tuning solution
170
preceded the start of the analytical measurements. The resulting performance report included
171
important sensitivity data, such as extent of formation of oxide ions (156CeO+/140Ce+) and
172
doubly charged ions (137Ba2+/137Ba+), in addition to count rates for the tuning solution
173
elements, whereby the optimisation status of the instrument could be assessed.
174
The nuclides measured, in order of mass number, were: 7Li, 11B, 24Mg, 27Al, 44Ca, 51V, 55Mn,
175
59
176
mostly metals and are considered to be useful as possible indicators of geographical origin
177
(Almeida & Vasconcelos, 2003; Thiel et al, 2004; Coetzee et al, 2005; Moreno et al, 2008;).
Co,
60
Ni,
65
Cu, 66Zn,
85
Rb,
88
Sr,
119
Cd,
133
Cs,
178 179
3. Results and Discussion 7
137
Ba,
205
Tl,
238
U. The elements selected are
180 181
3.1 Selection of elements
182
The 18 elements selected for this study in order of increasing atomic mass number, Li, B,
183
Mg, Al, Ca,V, Mn, Co, Ni, Cu, Zn, Rb, Sr, Cd, Cs, Ba, Tl, U, were chosen from the list of
184
elements shown in previous work on South African wines (Coetzee et al, 2005; Van der
185
Linde et al, 2010) to be useful indictors in wine provenance studies. The link between the
186
trace element composition of a wine and its provenance soil was verified for South African
187
wines for most elements in the list above (Van der Linde et al, 2010). ). Wine-making
188
technology can affect the trace element composition of a wine. Various studies have focused
189
on the effect of wine-making technologies on the elemental composition of a wine. Results
190
may vary from study to study and no consensus has been reached. A list of elements that
191
showed relatively small changes in concentration after bentonite treatment included the
192
elements Li, B, Mg, Ca, V, Mn, Fe, Co, Zn, Rb, Sr, Cs, Pb (Castiñeira Gómez Mdel, et al,
193
2004). Rare earths were found to be unsuitable indicator elements after bentonite treatment
194
(Jakubowski et al, 1999) because of large changes. In one study, it was found that B, K, Cu,
195
Zn, and Rb concentrations were actually decreased after bentonite treatment (Catarino et al,
196
2008). The selection of a set of indicator elements completely unaffected by the wine-making
197
process is not possible. In this work, the initial set of indicator elements was selected from
198
elements considered to be less prone to alteration by wine-making processes.
199 200
The average elemental concentrations for each estate are given in Table 2(a) and 2(b). The
201
concentrations of Co, Cd, and U were found to be less than the limit of quantification (LoQ)
202
of the method in the diluted wine matrix and were not used in data analysis.
203
/Insert Table 2/
204
8
205
3.2 QC Results
206
Quality control procedures included measures to ensure proper calibration of the instrument,
207
assessment of matrix effects, and interference checks. The relative percentage difference:
208
RPD = 100*(i ‒ r)/i
209
where i is the initial or known value and r is the repeat value calculated for each element,
210
between the repeat analyses of QC samples, was used to assess the validity of the
211
measurements.
212 213
Detector calibration was performed using two matrix-matched standard series with the
214
analytical ranges chosen to comply with the expected concentrations (Coetzee et al, 2005) of
215
each selected element in the wine. An initial calibration verification (ICV) sample, prepared
216
from a multi-element ICP standard (Merck), was included in the ICP-MS measurement
217
protocol following the external standards, to confirm the validity of the calibration.
218
Measurement was discontinued in case RPDs for the ICV > 10% were established.
219
Continuing calibration verification (CCV) samples and blank samples were measured after
220
each 10-sample cycle to assess possible instrument drift and build-up of cross-contamination
221
between samples. RPD values of < 5% were achieved for all elements except B and Ca at
3 wines per estate
287
criterion and no further data analysis was done on the white wine subset. A repeat of the
288
cluster analysis, on the red wine subset only, shows the 17 estates reporting membership in
289
three clusters. Only two wines, O2, and U6 report in a cluster different from the other wines
290
from the O and U estates.
291 292
Discriminant analysis (DA) of the red wine subset consisting of 88 wines from 17 estates was
293
done using loge-transformed concentrations of the PCA-selected elements plus. Loge-
294
transformed concentrations, bringing high and low abundances within the same range,
295
allowed element concentrations used in the statistical procedures to vary by orders of
296
magnitude. Kolmogorov-Smirnov analyses confirmed normality of the data with p-values >
297
0.05 in most cases. Pearson correlation analyses indicated acceptable linearity of the data
298
with a sufficient number of single element pairs showing significant correlations.
299
Importantly, no multicollinearity was observed. The result was confirmed using Spearman
300
correlation analysis.
301
A scatter plot, Figure 2, of the two discriminant functions shows a differentiation of the
302
estates into three groups. The grouping of the estates corresponds with the results obtained
303
using cluster analysis.
304
12
305
/Insert Figure 2/
306
A further DA was then performed on each of the clusters. In Figure 3, scatter plots, of the
307
discriminant functions obtained in this way, show the classification of wines from each estate
308
within a cluster. In all three clusters 95‒100% of the original grouped cases (estates) were
309
correctly classified. In cross-validation 80% of the cases were correctly classified in cluster 1
310
and 2 while 30% was correctly classified in cluster 3. In the latter case some wines were
311
erroneously classified in estate M with the largest number of wines. This, together with the
312
fact that the centroids of the estates M and SH lie close together in the scatter plot shown in
313
Figure 3, could contribute to the low percentage in cross-validation. Box’s test of covariance
314
matrices, however, failed in some cases to be as expected, because of the insufficient number
315
of wines available per estate.
316
/Insert Figure 3/
317
The complexity of the soil type distribution across wards, with similar soil types occurring in
318
different wards, precluded classification of the wine according to ward.
319
Nevertheless, the three clusters identified by applying cluster analysis and duplicated by
320
discriminant analysis, seem to coincide with particular wards and a set of soil types for that
321
area:
322
•
323 324
soil types F3/D3S/I1 •
325 326 327
Cluster 1: Blaauwklippen/ Helderberg wards including estates A, B, G, I, P and major Cluster 2: Stellenbosch West ward including estates C, E, V in the southern areas of the ward and major soil types F2/DIL/C2
•
Cluster 3: Simonsberg ward including estates M, R, U and major soil types F3/D4S
These clusters are indicated by ovals in Figure 1.
328 329
Stellenbosch West cellars in the northern area of the ward around Lyndoch, W and SH, were
330
classified in Cluster 3, where D4S soils predominate. Since D4S/D3S soils also occur in the
331
Lynedoch area, this classification seems plausible. Schaapenberg cellars, G and X, were
13
332
classified in Cluster 1 together with cellars from the Helderberg area. These areas, on the
333
south and north side of Helderberg mountain, share similar soil type distributions.
334 335
4. Conclusion
336 337
The results for the intraregional classification of wines from selected wards in the
338
Stellenbosch wine district in the Western Cape, suggest that classification of wines at estate
339
level is indeed possible using reliable multi-element chemical analysis and multivariate
340
statistical analysis based on a combination of cluster analysis (CA) and discriminant analysis
341
(DA). The statistical results, obtained from two independent multivariate procedures (DA and
342
CA), were in agreement and this strengthens the interpretation. The area from where the
343
wines were sourced was less than 1000 km2. To obtain successful classifications from such a
344
small area requires accurate analytical data. Much attention was therefore given to quality
345
control in the ICP-MS analysis procedures to ensure that the trace element concentrations of
346
the indicator elements are correct. In previous work the validity of this approach, to classify
347
wines according to geographical origin, was adequately proven for comparing wines from
348
different countries and different regions within one country. This work, for the first time,
349
demonstrates the applicability of the method for intraregional classification of wines from a
350
relatively small geographical area. Even more remarkable is the fact that classification of
351
wines pertaining to each estate was achieved. The result is evidently dependent on the
352
distribution of soil types in the area and, hence, the variability in trace element composition
353
of the soils. While exact correlation between soil type and wine classification was beyond the
354
scope of this work, three clusters in the area could be distinguished, corresponding with
355
combinations of major soil types found in the area.
14
356
It was demonstrated by CA that white wines, at the level of differences in trace element
357
composition encountered in the area, constitute a separate cluster and cannot be included in a
358
data set together with red wines. In interregional classifications where differences in soil
359
composition are larger, it was found that red and white wines could be grouped together in
360
one data set to enable successful classification.
361 362
Acknowledgements
363 364
The authors thank Francois October from ARC-Nietvoorbij Stellenbosch, for sourcing and
365
collecting wine samples and Juliana van Staden from STATKON, University of
366
Johannesburg Statistical Services, for performing multivariate statistical analysis of the data.
367 368 369
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Gonzalez, A.G., & Hardisson, A. (2008). Determination of Al, Ba, Ca, Cu, Fe, K, Mg,
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Mn, Na, Sr and Zn in red wine samples by inductively coupled plasma optical emission
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spectroscopy: Evaluation of preliminary sample treatments. Microchemical Journal, 88,
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23. Orescanin, V., Katunar, A., Kutle, A., & Valkovic, V. (2003). Heavy metals in soil, grape, and wine. Journal of Trace and Microprobe Techniques, 21, 171-180.
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24. Serapinas, P., Venskutonis, P.R., Aninkevičius, V., Ežerinskis, Ž., Galdikas, A., &
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Juzikienė, V. (2008). Step by step approach to multi-element data analysis in testing the
448 449
provenance of wines. Food Chemistry, 107, 1652-1660.
450 451 452 453
25. Suhaj, M., & Korenovská, M. (2005). Application of elemental analysis for identification of wine origin: A review. Acta Alimentaria, 34, 393-401. 26. Schwartz, R.S., & Hecking, L.T. (1991). Determination of geographic origin of agricultural products by multivariate-analysis of trace element composition. Journal of Analytical Atomic Spectrometry, 6, 637-642.
18
454
27. Tarantilis, P.A., Troianou, V.E., Pappas, C.S., Kotseridis, Y.S., & Polissiou, M.G.
455
(2008). Differentiation of Greek red wines on the basis of grape variety using attenuated
456
total reflectance Fourier transform infrared spectroscopy. Food Chemistry, 111, 192–
457
196.
458
28. Taylor, V.F., Longerich, H.P., & Greenough, J.D. (2003). Multielement analysis of
459
Canadian wines by inductively coupled plasma mass spectrometry (ICP-MS) and
460
multivariate statistics. Journal of Agricultural and Food Chemistry, 51, 856-860.
461
29. Thiel, G., Geisler, G., & Blechschmidt, I. (2004). Determination of trace elements in
462
wines and classification according to their provenance. Analytical and Bioanalytical
463
Chemistry, 378, 1630-1636.
464
30. Tzouros, N. E., & Arvanitoyannis I. S. (2001). Agricultural produces: Synopsis of
465
employed quality control methods for the authentication of foods and application of
466
chemometrics for the classification of foods. Critical Reviews in Food Science and
467
Nutrition, 41, 287-319.
468
31. Van der Linde, G., Fischer J.L. & Coetzee, P.P. (2010). Multi-element analysis of South
469
African wines and their provenance soils by ICP-MS and their classification according
470
to geographical origin using multivariate statistics. South African Journal of Enolology
471
and Viticulture, 31, 143-153.
472
32. Vorster, C., Greeff, L., & Coetzee, P.P. (2010). The determination of
473
87
474
African wine. South African Journal of Chemistry, 63, 207–214.
11
B/10B and
Sr/86Sr isotope ratios by quadrupole-based ICP-MS for the fingerprinting of South
475 476 477 478 479 480
Figure Captions Figure 1. Map of soil type distribution in the sampling area. The map area represents ca 1000 km2. The ovals indicate the position of the clusters as identified by cluster analysis and discriminant analysis: Cluster 1 incorporating the wines from the wards Helderberg,
19
481 482 483 484 485 486 487 488 489 490 491
Blaauwklippen and Schaapenberg, Cluster 2 incorporating the wines from the Stellenbosch West ward, and Cluster 3 incorporating the wines from the Simonsberg ward. Figure 2. Canonical discriminant functions plot for red wines from 17 estates showing estates grouped into three clusters. Figure 3. Canonical discriminant functions plot for red wines from 17 estates showing estates reporting in (a) Cluster 1, (b) Cluster 2 (c) Cluster 3.
20
492 493
21
494 495
22
496 497
23
498
499
500 501 24
502 503 504
Table 1. Sample details of the sourced wines, listing cellars, cellar codes, and major soil type codes of the wards. ward/pocket
soil type
estate/cellar
cellar code
red wine
white wine
F3 D4S I1
Blaauwklippen Dornier Kleine Zalze Stellenzicht Waterford
B I Z S T
6 6 2 5 1
1 1 3
F3 D3S I1 C2
Avontuur Croyden Post House Ridgemor
A C P C
4 6 6 1
F2/F3 I1
Morgenster Vergelegen
G X
10
F3 D4S C2
Delheim L’Avenir Morgenhof Rustenberg Uitkyk
D L M R U
3 4 6 4 4
3 2
N O SH W
3 3 4 4
2 3
E E V
1 4 6
1 1
Blaauwklippen
Helderberg 2 1
Schaapenberg 3
Simonsberg
Stellenbosch West-Lynedoch D3S/D4S Neethlingshof Overgaauw I1 Stellenbosch Hills Welmoed Stellenbosch West-Faure I1 The Foundry F2 Meerlust DIL Vergenoegd
505 506
25
2 2
507
Table 2(a). Average elemental concentrations (µ µg/L) per estate/cellar. Light elements. Estate MDL B S I E G C P A D L M R U SH V W O
508 509
Li
0.02 0.19±0.09 0.15±0.01 0.19±0.10 0.23±0.03 0.10±0.02 0.15±0.06 0.08±0.01 0.20±0.18 0.19±0.04 0.19±0.05 0.12±0.03 0.04±0.01 0.24±0.09 0.12±0.05 0.42±0.13 0.22±0.13 0.22±0.21
B
Mg
15 746±117 479±30 633±115 632±76 579±51 603±60 513±32 503±105 762±105 852±103 636±84 612±119 672±54 657±79 839±117 860±160 674±257
2.5 12045±1947 12686±1647 12500±2210 14024±1844 11141±1211 11670±1241 11150±1061 11170±1364 12717±1094 11915±1412 13418±1527 12428±2493 13658±1803 11309±1209 10978±951 12333±1030 9711±1258
Al 0.43 32.7±7.6 64.0±20.5 19.1±7.1 49.7±11.8 24.5±3.7 43.3±9.0 36.3±7.7 27.1±5.5 45.3±6.6 39.8±12.1 21.4±5.3 22.5±4.8 27.3±14.9 20.9±3.0 26.3±10.9 28.1±3.4 47.2±36.3
Ca
270 8499±796 8453±756 9788±743 7479±1817 10343±557 7636±1716 7678±1528 9065±649 9839±811 6174±132 4197±419 3961±833 3789±766 4086±538 4859±1820 4258±477 4332±277
V
0.01 0.06±0.01 0.46±0.83 0.05±0.01 0.18±0.05 0.06±0.01 0.18±0.02 0.08±0.02 0.07±0.01 0.86±0.37 1.40±1.13 0.04±0.01 0.03±0.01 1.01±1.89 0.04±0.01 0.32±0.40 0.08±0.02 0.32±0.42
Mn 0.07 169±22 184±35 137±12 228±55 56±20 184±21 105±23 190±113 203±28 187±32 130±40 180±61 167±28 97±20 172±32 108±22 94±44
Table 2(b). Average elemental concentrations (µ µg/L) per estate/cellar. Heavy elements. Estate MDL B S I E G C P A D L M R U N SH V W O
Ni 0.07 0.84±0.14 1.15±0.19 1.11±1.29 1.38±0.36 1.40±0.20 1.37±0.28 0.57±0.11 0.42±0.06 0.45±0.06 0.95±0.19 0.68±0.21 0.43±0.12 0.89±0.23 0.42±0.12 0.59±0.07 1.26±0.45 0.60±0.04 1.01±0.50
Cu 0.16 4.12±1.91 7.58±5.38 8.38±4.98 4.32±1.87 10.5±5.61 3.22±1.51 11.5±5.1 10.7±5.3 4.43±2.31 5.04±1.41 2.79±1.13 1.24±1.21 21.1±4.1 8.87±0.50 10.7±1.4 6.44±2.94 1.26±0.13 6.10±2.25
Zn 0.11 23±10 84±24 81±23 76±4 41±11 50±18 23±19 32±13 30±5 82±23 35±10 44±26 65±11 84±25 45±12 62±14 38±11 62±6
Rb 0.04 282±61 442±117 451±238 271±140 291±93 176±67 227±82 199±40 477±75 259±19 242±68 568±217 630±89 464±10 242±78 229±38 416±142 412±251
510 511
26
Sr 0.02 42±4 64±24 41±11 107±17 86±18 102±18 70±19 50±5 50±9 62±15 55±19 54±16 84±10 77±1 65±21 97±10 59±14 56±29
Cs 0.001 0.79±0.34 1.77±0.79 1.65±0.77 1.03±0.92 1.82±1.33 0.30±0.21 0.42±0.14 0.91±0.12 0.90±0.07 0.44±0.10 0.91±0.38 3.06±2.07 1.60±0.10 1.79±0.41 1.43±1.09 0.86±0.44 6.71±4.11 1.22±0.72
Ba 0.02 4.80±1.15 9.52±2.80 5.67±2.54 21.8±3.1 15.4±4.4 10.3±5.8 10.6±1.8 8.03±3.82 14.2±3.4 17.2±3.1 22.2±9.1 48.3±24.2 29.4±2.1 17.2±6.1 14.7±4.7 12.1±2.2 11.8±5.1 12.0±8.1
Tl 0.003 0.028±0.009 0.072±0.016 0.032±0.014 0.071±0.031 0.029±0.016 0.027±0.051 0.035±0.020 0.061±0.041 0.037±0.042 0.004±0.008 0.062±0.021 0.073±0.032 0.071±0.013 0.153±0.042 0.114±0.082 0.064±0.023 0.096±0.010 0.056±0.013
512 513 514 515 516 517 518
Highlights • • • •
Intraregional classification of wines according to estate by elemental fingerprinting. Wines sourced from estates within a small wine-producing area < 1000 km2. Red and white wines grouped in separate data sets for statistical analysis. Correlation between soil type distributions and wine classification observed.
27
S0308-8146(14)00731-6 http://dx.doi.org/10.1016/j.foodchem.2014.05.027 FOCH 15808
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
24 January 2014 25 April 2014 10 May 2014
Please cite this article as: Coetzee, P.P., van Jaarsveld, F.P., Vanhaecke, F., Intraregional Classification of Wine via ICP-MS elemental Fingerprinting, Food Chemistry (2014), doi: http://dx.doi.org/10.1016/j.foodchem.2014.05.027
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1
Intraregional Classification of Wine via ICP-MS elemental
2
Fingerprinting.
3 P.P. Coetzeea,*, F.P. van Jaarsveld,b F. Vanhaeckec
4 5 6 7 8 9 10
a
Department of Chemistry, University of Johannesburg, Johannesburg, Box 524, Johannesburg 2006, South
Africa. E-mail: [email protected] b
ARC
Infruitec-Nietvoorbij,
Private
Bag
X5026,
Stellenbosch
7599,
South
Africa.
E-mail:
[email protected] c
Department of Analytical Chemistry, Krijgslaan 281-S12, Ghent University, B-9000 Ghent, Belgium. E-mail:
[email protected]
11 12
*Corresponding author. E-mail: [email protected]; Tel: +27(0)11 5592558; Fax: +27 (0)11 5592819
13 14
Abstract
15
The feasibility of elemental fingerprinting in the classification of wines according to their
16
provenance vineyard soil was investigated in the relatively small geographical area of a
17
single wine district. Results for the Stellenbosch wine district (Western Cape Wine Region,
18
South Africa), comprising an area of less than 1000 km2, suggest that classification of wines
19
from different estates (120 wines from 23 estates) is indeed possible using accurate elemental
20
data and multivariate statistical analysis based on a combination of principal component
21
analysis, cluster analysis, and discriminant analysis. This is the first study to demonstrate the
22
successful classification of wines at estate level in a single wine district in South Africa. The
23
elements B, Ba, Cs, Cu, Mg, Rb, Sr, Tl, and Zn were identified as suitable indicators. White
24
and red wines were grouped in separate data sets to allow successful classification of wines.
25
Correlation between wine classification and soil type distributions in the area was observed.
26 27
Keywords: ICP-MS; multi-element analysis of wine; wine provenance; multivariate
28
statistical analysis 1
29
Abbreviated running title: Intraregional Wine Fingerprinting.
30 31
1. Introduction
32 33
In recent years, much progress has been made in food authentication through fingerprinting
34
techniques (Kelly et al, 2005), in particular in terms of provenance determination. The
35
method combines chemical analysis, by any of a variety of instrumental analytical techniques
36
(primarily trace element and isotope ratio analysis), and multivariate statistical analysis
37
(Tzouros & Arvanitoyannis, 2001; Serapinas et al., 2008) of the chemical data, to obtain
38
identification and classification of an agricultural product according to geographical origin.
39
The method assumes that the chemical composition of an agricultural product, such as wine,
40
will reflect the composition of the provenance soil. In the case of wine, studies of this nature
41
are being pursued in most wine-producing countries (Martin et al., 1999; Taylor et al., 2003;
42
Gremaud et al., 2004; Thiel et al., 2004; Angus et al., 2006; Tarantilis et al., 2008; Galgano
43
et al., 2008; Gonzálvez et al., 2009; Laurie et al.; 2010; Fabani et al., 2010; Fabrina et al.,
44
2011; Di Paola-Naranjo et al., 2011). These studies focus on distinguishing wines from
45
different countries and some also attempt to classify wines from different regions in one
46
country. The extensive literature, covering the progress made in this field since the idea was
47
first explored in the 1990’s (Swartz & Hecking, 1991; Baxter et al, 1997), is reviewed
48
elsewhere (Suhaj & Korenovská, 2005; Capron, 2007; Giaccio & Vicentini, 2008;) and is not
49
discussed here.
50
In previous work (Coetzee et al., 2005; Coetzee & Vanhaecke, 2005; Vorster et al., 2010;
51
Coetzee et al., 2011) it was demonstrated that South African wines can be distinguished from
52
wines from Europe and that the interregional classification of wines within South Africa, is
53
possible using ICP-MS for multi-element and isotopic analysis (11B/10B ratio). The
2
54
correlation between the trace element composition of a wine and that of the provenance soil
55
was verified (Van der Linde et al., 2010).
56 57
In the current work, intraregional classification of wines from one region or part of a region
58
only, was investigated, in order to establish the limits and reliability of the application in the
59
relative small geographical area of a single wine district. The main objective was to ascertain
60
whether differentiation was possible at ward level or even at estate level. A section of the
61
Stellenbosch wine district, ranging from the Helderberg mountain in the south to the
62
Simonsberg mountain in the north and covering an area of about 1000 km2, was selected for
63
the study (See Figure 1). The area is characterised by a complex distribution of soil types and
64
this was taken into account in the selection of wards and estates included in this study. The
65
smaller the geographical demarcation is, the higher are the demands on the accuracy and
66
precision of the analytical data obtained by ICP-MS for successful classification. Diligent
67
application of quality control procedures to ensure reliable analytical data was therefore
68
required.
69 70
2. Materials and Methods
71 72
2.1 Wine samples
73
A total of 120 wine samples (93 red, 27 white), from 5 wards in the Stellenbosch area in the
74
Western Cape Wine Region, was collected from 23 estates and wine cellars during the first
75
quarter of 2010. In order to obtain a sufficient number of samples per estate, wines were
76
selected from the 2001 to 2010 vintages, depending on the availability of suitable samples at
77
the respective estates and cellars. The red cultivars consisted of Cabernet Sauvignon,
78
Cabernet Franc, Malbec, Merlot, Petit Verdot, Pinot Noir, Pinotage, and Shiraz. The white
3
79
cultivars were Chardonnay, Sauvignon Blanc, Chenin Blanc, Gewürztraminer, Semillon,
80
Viognier, and Weisser Riesling. All wines were made from grapes produced in identified
81
blocks pertaining to the particular estates. Table 1 lists the wards and estates/cellars plus
82
cellar codes and also gives the major soil types associated with the wards from where the
83
wines were sourced.
84
/ Insert Table 1 /
85
2.2 Soil characteristics
86
In previous work (Van der Linde et al., 2010), the link between the trace element
87
composition in a wine and that in the provenance soil was established for South African
88
wines. This is an essential precondition for the application of multi-element data and
89
multivariate statistical analysis to the classification of wines according to geographical origin.
90
In this work the complexity of soil type distributions in the Stellenbosch area was taken into
91
account in selecting wines from wards representative of different soil types.
92
The distribution of the soil types (Hartmann, 1969) in the sampling area is indicated by soil
93
type codes in Figure 1.
94
Three major soil types occur in the area where the wines were sourced.
95
These are:
96
• red kaolinic loams and clays of the granitic foothills (Code F2 and F3), for example
97
found in the Simonsberg-Stellenbosch ward on the western foothills of the
98
Simonsberg.
99
• duplex soils, mostly coarse alluvial sands and residual clays (Code D4S and D3S),
100
derived from granite and shale, for example found in the Stellenbosch-West ward.
101
• weakly developed hydromorphic soils derived from alluvial and fluvial sandy deposits
102
along rivers and streams (Code I1), for example, areas along the Eerste River in the
103
Stellenbosch West ward.
104
4
105
Small pockets of poorly developed loam shale-based soils (C2) and duplex fine sand or loam
106
top soils (DIL) are dispersed among the three major soil types contributing to the overall
107
complexity. These pockets are found in the Eerste River Valley between Lynedoch and the
108
river mouth in False Bay.
109
/ Insert Figure 1 /
110
It is clear from this picture that a ward could include any combination of three or more
111
different soil types. The approximate positions of the wards where wines were sourced are
112
indicated by ovals in Figure 1. The soil type alone is not necessarily an indication that trace
113
element differences between soil types would be sufficient to allow for successful provenance
114
determination. How this variability reflects the trace element composition of the soil is not
115
generally known. It was the aim of this work to establish whether these differences would
116
allow classification of wines according to estate.
117 118
2.3 Sample preparation
119
Volumetric equipment was soaked in 1% distilled HNO3 overnight and thoroughly rinsed
120
with Milli-Q water, before use. High purity 18 MΩ.cm water was obtained from a Milli-Q
121
purification system and was used in the preparation of all solutions. HNO3 was purified by
122
distillation in a sub-boiling quartz distillation system. High-purity ethanol was used for
123
preparing matrix-matched standards.
124 125
Wine samples were diluted 1:10 with distilled 1% HNO3 and indium was added as internal
126
standard (final concentration: 100 µg/L). The dilution reduced the ethanol concentration to
127
between 1.2 and 1.5% and residual sugar to < 0.3 g/L or 0.03% (because only wines
128
classified as “dry” were sourced), which was sufficiently low to diminish matrix effects and
129
plasma instability caused by organic matrices in the plasma during measurement.
5
130 131
The method blank was a 1.2% ethanol/1% HNO3 solution containing 100 µg/L of the In
132
internal standard. Standards were prepared in 1.2% ethanol/1% HNO3 by appropriate dilution
133
of 1000 mg/L single element Merck ICP standard stock solutions and the addition of 100
134
µg/L of the In internal standard. Two matrix-matched calibration standard series were
135
prepared to match the expected concentration ranges in wine of the 18 elements selected for
136
this study: Low Standard series: Al, B, Ba, Cu, Mn, Rb, Sr, Zn at 50, 250, 500 µg/L and Cd,
137
Co, Cs, Li, Ni, Tl, U, V at 5, 25, 50 µg/L. High Standard series: Ca, Mg at 5, 25, 50 mg/L
138 139
2.3 Quality control (QC)
140
A 100 µg/L Initial Calibration Verification (ICV) standard in 1.2% ethanol/1% HNO3 was
141
prepared from 1000 µg/L multi-element ICP standard (Merck) containing the elements: Ag,
142
Al, B, Ba, Bi, Ca, Cd, Co, Cr, Cu, Fe, Ga, In, K, Li, Mg, Mn, Na, Ni, Pb, Sr, Tl, Zn. The
143
multi-element standard already contained In, therefore no additional internal standard was
144
added.
145 146
Continuing calibration verification (CCV) standards were prepared from single element ICP
147
standards (Merck) consisting of 20 mg/L Ca, Mg for the high standard series and 250 µg/L
148
Al, B, Ba, Cu, Mn, Rb, Sr, Zn and 25 µg/L Cd, Co, Cs, Li, Ni, Tl, U, V, for the low standard
149
series. The CCV’s were measured after every 10 samples.
150 151
Duplicates of two white and two red wine samples were prepared.
152 153
Possible matrix effects were checked by running an interference check sample (ICS)
154
consisting of Ca (50 mg/L), Na (100 mg/L), Mg (250 mg/L), K (500mg/L). In addition, 6
155
spike recovery tests and serial dilutions were done on red wine and white wine samples. The
156
spiked samples were prepared at concentration levels of 20 and 100 µg/L for the elements Al,
157
Ba, Cu, and Sr and of 100 and 500 µg/L for the elements B, Mn, Rb and Zn. A serial dilution
158
check (1:10 followed by 1:3, thus 1:30 final dilution) was done on one white wine and one
159
red wine.
160 161
2.4 Instrumentation
162
Analyses were carried out with a quadrupole-based Thermo X-Series 2 inductively coupled
163
plasma-mass spectrometer (ICP-MS) equipped with nickel cones, a Peltier-cooled, low-
164
volume conical spray chamber fitted with a fixed impact bead and a high-performance glass
165
concentric nebuliser, a peristaltic sample delivery pump, and a Cetac 500 autosampler.
166
Instrument operating conditions were optimised for analysis of the diluted wine samples.
167
Mass calibration and detector cross-calibration were performed according to the instrument
168
manufacturer’s instructions, using the prescribed solutions obtained from Thermo. A
169
sensitivity check using a 10 µg/L Ba, Be, Bi, Ce, Co, In, Li, Ni, Pb, U tuning solution
170
preceded the start of the analytical measurements. The resulting performance report included
171
important sensitivity data, such as extent of formation of oxide ions (156CeO+/140Ce+) and
172
doubly charged ions (137Ba2+/137Ba+), in addition to count rates for the tuning solution
173
elements, whereby the optimisation status of the instrument could be assessed.
174
The nuclides measured, in order of mass number, were: 7Li, 11B, 24Mg, 27Al, 44Ca, 51V, 55Mn,
175
59
176
mostly metals and are considered to be useful as possible indicators of geographical origin
177
(Almeida & Vasconcelos, 2003; Thiel et al, 2004; Coetzee et al, 2005; Moreno et al, 2008;).
Co,
60
Ni,
65
Cu, 66Zn,
85
Rb,
88
Sr,
119
Cd,
133
Cs,
178 179
3. Results and Discussion 7
137
Ba,
205
Tl,
238
U. The elements selected are
180 181
3.1 Selection of elements
182
The 18 elements selected for this study in order of increasing atomic mass number, Li, B,
183
Mg, Al, Ca,V, Mn, Co, Ni, Cu, Zn, Rb, Sr, Cd, Cs, Ba, Tl, U, were chosen from the list of
184
elements shown in previous work on South African wines (Coetzee et al, 2005; Van der
185
Linde et al, 2010) to be useful indictors in wine provenance studies. The link between the
186
trace element composition of a wine and its provenance soil was verified for South African
187
wines for most elements in the list above (Van der Linde et al, 2010). ). Wine-making
188
technology can affect the trace element composition of a wine. Various studies have focused
189
on the effect of wine-making technologies on the elemental composition of a wine. Results
190
may vary from study to study and no consensus has been reached. A list of elements that
191
showed relatively small changes in concentration after bentonite treatment included the
192
elements Li, B, Mg, Ca, V, Mn, Fe, Co, Zn, Rb, Sr, Cs, Pb (Castiñeira Gómez Mdel, et al,
193
2004). Rare earths were found to be unsuitable indicator elements after bentonite treatment
194
(Jakubowski et al, 1999) because of large changes. In one study, it was found that B, K, Cu,
195
Zn, and Rb concentrations were actually decreased after bentonite treatment (Catarino et al,
196
2008). The selection of a set of indicator elements completely unaffected by the wine-making
197
process is not possible. In this work, the initial set of indicator elements was selected from
198
elements considered to be less prone to alteration by wine-making processes.
199 200
The average elemental concentrations for each estate are given in Table 2(a) and 2(b). The
201
concentrations of Co, Cd, and U were found to be less than the limit of quantification (LoQ)
202
of the method in the diluted wine matrix and were not used in data analysis.
203
/Insert Table 2/
204
8
205
3.2 QC Results
206
Quality control procedures included measures to ensure proper calibration of the instrument,
207
assessment of matrix effects, and interference checks. The relative percentage difference:
208
RPD = 100*(i ‒ r)/i
209
where i is the initial or known value and r is the repeat value calculated for each element,
210
between the repeat analyses of QC samples, was used to assess the validity of the
211
measurements.
212 213
Detector calibration was performed using two matrix-matched standard series with the
214
analytical ranges chosen to comply with the expected concentrations (Coetzee et al, 2005) of
215
each selected element in the wine. An initial calibration verification (ICV) sample, prepared
216
from a multi-element ICP standard (Merck), was included in the ICP-MS measurement
217
protocol following the external standards, to confirm the validity of the calibration.
218
Measurement was discontinued in case RPDs for the ICV > 10% were established.
219
Continuing calibration verification (CCV) samples and blank samples were measured after
220
each 10-sample cycle to assess possible instrument drift and build-up of cross-contamination
221
between samples. RPD values of < 5% were achieved for all elements except B and Ca at
3 wines per estate
287
criterion and no further data analysis was done on the white wine subset. A repeat of the
288
cluster analysis, on the red wine subset only, shows the 17 estates reporting membership in
289
three clusters. Only two wines, O2, and U6 report in a cluster different from the other wines
290
from the O and U estates.
291 292
Discriminant analysis (DA) of the red wine subset consisting of 88 wines from 17 estates was
293
done using loge-transformed concentrations of the PCA-selected elements plus. Loge-
294
transformed concentrations, bringing high and low abundances within the same range,
295
allowed element concentrations used in the statistical procedures to vary by orders of
296
magnitude. Kolmogorov-Smirnov analyses confirmed normality of the data with p-values >
297
0.05 in most cases. Pearson correlation analyses indicated acceptable linearity of the data
298
with a sufficient number of single element pairs showing significant correlations.
299
Importantly, no multicollinearity was observed. The result was confirmed using Spearman
300
correlation analysis.
301
A scatter plot, Figure 2, of the two discriminant functions shows a differentiation of the
302
estates into three groups. The grouping of the estates corresponds with the results obtained
303
using cluster analysis.
304
12
305
/Insert Figure 2/
306
A further DA was then performed on each of the clusters. In Figure 3, scatter plots, of the
307
discriminant functions obtained in this way, show the classification of wines from each estate
308
within a cluster. In all three clusters 95‒100% of the original grouped cases (estates) were
309
correctly classified. In cross-validation 80% of the cases were correctly classified in cluster 1
310
and 2 while 30% was correctly classified in cluster 3. In the latter case some wines were
311
erroneously classified in estate M with the largest number of wines. This, together with the
312
fact that the centroids of the estates M and SH lie close together in the scatter plot shown in
313
Figure 3, could contribute to the low percentage in cross-validation. Box’s test of covariance
314
matrices, however, failed in some cases to be as expected, because of the insufficient number
315
of wines available per estate.
316
/Insert Figure 3/
317
The complexity of the soil type distribution across wards, with similar soil types occurring in
318
different wards, precluded classification of the wine according to ward.
319
Nevertheless, the three clusters identified by applying cluster analysis and duplicated by
320
discriminant analysis, seem to coincide with particular wards and a set of soil types for that
321
area:
322
•
323 324
soil types F3/D3S/I1 •
325 326 327
Cluster 1: Blaauwklippen/ Helderberg wards including estates A, B, G, I, P and major Cluster 2: Stellenbosch West ward including estates C, E, V in the southern areas of the ward and major soil types F2/DIL/C2
•
Cluster 3: Simonsberg ward including estates M, R, U and major soil types F3/D4S
These clusters are indicated by ovals in Figure 1.
328 329
Stellenbosch West cellars in the northern area of the ward around Lyndoch, W and SH, were
330
classified in Cluster 3, where D4S soils predominate. Since D4S/D3S soils also occur in the
331
Lynedoch area, this classification seems plausible. Schaapenberg cellars, G and X, were
13
332
classified in Cluster 1 together with cellars from the Helderberg area. These areas, on the
333
south and north side of Helderberg mountain, share similar soil type distributions.
334 335
4. Conclusion
336 337
The results for the intraregional classification of wines from selected wards in the
338
Stellenbosch wine district in the Western Cape, suggest that classification of wines at estate
339
level is indeed possible using reliable multi-element chemical analysis and multivariate
340
statistical analysis based on a combination of cluster analysis (CA) and discriminant analysis
341
(DA). The statistical results, obtained from two independent multivariate procedures (DA and
342
CA), were in agreement and this strengthens the interpretation. The area from where the
343
wines were sourced was less than 1000 km2. To obtain successful classifications from such a
344
small area requires accurate analytical data. Much attention was therefore given to quality
345
control in the ICP-MS analysis procedures to ensure that the trace element concentrations of
346
the indicator elements are correct. In previous work the validity of this approach, to classify
347
wines according to geographical origin, was adequately proven for comparing wines from
348
different countries and different regions within one country. This work, for the first time,
349
demonstrates the applicability of the method for intraregional classification of wines from a
350
relatively small geographical area. Even more remarkable is the fact that classification of
351
wines pertaining to each estate was achieved. The result is evidently dependent on the
352
distribution of soil types in the area and, hence, the variability in trace element composition
353
of the soils. While exact correlation between soil type and wine classification was beyond the
354
scope of this work, three clusters in the area could be distinguished, corresponding with
355
combinations of major soil types found in the area.
14
356
It was demonstrated by CA that white wines, at the level of differences in trace element
357
composition encountered in the area, constitute a separate cluster and cannot be included in a
358
data set together with red wines. In interregional classifications where differences in soil
359
composition are larger, it was found that red and white wines could be grouped together in
360
one data set to enable successful classification.
361 362
Acknowledgements
363 364
The authors thank Francois October from ARC-Nietvoorbij Stellenbosch, for sourcing and
365
collecting wine samples and Juliana van Staden from STATKON, University of
366
Johannesburg Statistical Services, for performing multivariate statistical analysis of the data.
367 368 369
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B/10B and
Sr/86Sr isotope ratios by quadrupole-based ICP-MS for the fingerprinting of South
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Figure Captions Figure 1. Map of soil type distribution in the sampling area. The map area represents ca 1000 km2. The ovals indicate the position of the clusters as identified by cluster analysis and discriminant analysis: Cluster 1 incorporating the wines from the wards Helderberg,
19
481 482 483 484 485 486 487 488 489 490 491
Blaauwklippen and Schaapenberg, Cluster 2 incorporating the wines from the Stellenbosch West ward, and Cluster 3 incorporating the wines from the Simonsberg ward. Figure 2. Canonical discriminant functions plot for red wines from 17 estates showing estates grouped into three clusters. Figure 3. Canonical discriminant functions plot for red wines from 17 estates showing estates reporting in (a) Cluster 1, (b) Cluster 2 (c) Cluster 3.
20
492 493
21
494 495
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496 497
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500 501 24
502 503 504
Table 1. Sample details of the sourced wines, listing cellars, cellar codes, and major soil type codes of the wards. ward/pocket
soil type
estate/cellar
cellar code
red wine
white wine
F3 D4S I1
Blaauwklippen Dornier Kleine Zalze Stellenzicht Waterford
B I Z S T
6 6 2 5 1
1 1 3
F3 D3S I1 C2
Avontuur Croyden Post House Ridgemor
A C P C
4 6 6 1
F2/F3 I1
Morgenster Vergelegen
G X
10
F3 D4S C2
Delheim L’Avenir Morgenhof Rustenberg Uitkyk
D L M R U
3 4 6 4 4
3 2
N O SH W
3 3 4 4
2 3
E E V
1 4 6
1 1
Blaauwklippen
Helderberg 2 1
Schaapenberg 3
Simonsberg
Stellenbosch West-Lynedoch D3S/D4S Neethlingshof Overgaauw I1 Stellenbosch Hills Welmoed Stellenbosch West-Faure I1 The Foundry F2 Meerlust DIL Vergenoegd
505 506
25
2 2
507
Table 2(a). Average elemental concentrations (µ µg/L) per estate/cellar. Light elements. Estate MDL B S I E G C P A D L M R U SH V W O
508 509
Li
0.02 0.19±0.09 0.15±0.01 0.19±0.10 0.23±0.03 0.10±0.02 0.15±0.06 0.08±0.01 0.20±0.18 0.19±0.04 0.19±0.05 0.12±0.03 0.04±0.01 0.24±0.09 0.12±0.05 0.42±0.13 0.22±0.13 0.22±0.21
B
Mg
15 746±117 479±30 633±115 632±76 579±51 603±60 513±32 503±105 762±105 852±103 636±84 612±119 672±54 657±79 839±117 860±160 674±257
2.5 12045±1947 12686±1647 12500±2210 14024±1844 11141±1211 11670±1241 11150±1061 11170±1364 12717±1094 11915±1412 13418±1527 12428±2493 13658±1803 11309±1209 10978±951 12333±1030 9711±1258
Al 0.43 32.7±7.6 64.0±20.5 19.1±7.1 49.7±11.8 24.5±3.7 43.3±9.0 36.3±7.7 27.1±5.5 45.3±6.6 39.8±12.1 21.4±5.3 22.5±4.8 27.3±14.9 20.9±3.0 26.3±10.9 28.1±3.4 47.2±36.3
Ca
270 8499±796 8453±756 9788±743 7479±1817 10343±557 7636±1716 7678±1528 9065±649 9839±811 6174±132 4197±419 3961±833 3789±766 4086±538 4859±1820 4258±477 4332±277
V
0.01 0.06±0.01 0.46±0.83 0.05±0.01 0.18±0.05 0.06±0.01 0.18±0.02 0.08±0.02 0.07±0.01 0.86±0.37 1.40±1.13 0.04±0.01 0.03±0.01 1.01±1.89 0.04±0.01 0.32±0.40 0.08±0.02 0.32±0.42
Mn 0.07 169±22 184±35 137±12 228±55 56±20 184±21 105±23 190±113 203±28 187±32 130±40 180±61 167±28 97±20 172±32 108±22 94±44
Table 2(b). Average elemental concentrations (µ µg/L) per estate/cellar. Heavy elements. Estate MDL B S I E G C P A D L M R U N SH V W O
Ni 0.07 0.84±0.14 1.15±0.19 1.11±1.29 1.38±0.36 1.40±0.20 1.37±0.28 0.57±0.11 0.42±0.06 0.45±0.06 0.95±0.19 0.68±0.21 0.43±0.12 0.89±0.23 0.42±0.12 0.59±0.07 1.26±0.45 0.60±0.04 1.01±0.50
Cu 0.16 4.12±1.91 7.58±5.38 8.38±4.98 4.32±1.87 10.5±5.61 3.22±1.51 11.5±5.1 10.7±5.3 4.43±2.31 5.04±1.41 2.79±1.13 1.24±1.21 21.1±4.1 8.87±0.50 10.7±1.4 6.44±2.94 1.26±0.13 6.10±2.25
Zn 0.11 23±10 84±24 81±23 76±4 41±11 50±18 23±19 32±13 30±5 82±23 35±10 44±26 65±11 84±25 45±12 62±14 38±11 62±6
Rb 0.04 282±61 442±117 451±238 271±140 291±93 176±67 227±82 199±40 477±75 259±19 242±68 568±217 630±89 464±10 242±78 229±38 416±142 412±251
510 511
26
Sr 0.02 42±4 64±24 41±11 107±17 86±18 102±18 70±19 50±5 50±9 62±15 55±19 54±16 84±10 77±1 65±21 97±10 59±14 56±29
Cs 0.001 0.79±0.34 1.77±0.79 1.65±0.77 1.03±0.92 1.82±1.33 0.30±0.21 0.42±0.14 0.91±0.12 0.90±0.07 0.44±0.10 0.91±0.38 3.06±2.07 1.60±0.10 1.79±0.41 1.43±1.09 0.86±0.44 6.71±4.11 1.22±0.72
Ba 0.02 4.80±1.15 9.52±2.80 5.67±2.54 21.8±3.1 15.4±4.4 10.3±5.8 10.6±1.8 8.03±3.82 14.2±3.4 17.2±3.1 22.2±9.1 48.3±24.2 29.4±2.1 17.2±6.1 14.7±4.7 12.1±2.2 11.8±5.1 12.0±8.1
Tl 0.003 0.028±0.009 0.072±0.016 0.032±0.014 0.071±0.031 0.029±0.016 0.027±0.051 0.035±0.020 0.061±0.041 0.037±0.042 0.004±0.008 0.062±0.021 0.073±0.032 0.071±0.013 0.153±0.042 0.114±0.082 0.064±0.023 0.096±0.010 0.056±0.013
512 513 514 515 516 517 518
Highlights • • • •
Intraregional classification of wines according to estate by elemental fingerprinting. Wines sourced from estates within a small wine-producing area < 1000 km2. Red and white wines grouped in separate data sets for statistical analysis. Correlation between soil type distributions and wine classification observed.
27
