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Metal content in southern Spain wines and their classification according to origin and ageing
Microchemical Journal 94 (2010) 175–179
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Microchemical Journal j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / m i c r o c
Metal content in southern Spain wines and their classification according to origin and ageing Patricia Paneque a,⁎, Ma Teresa Álvarez-Sotomayor b, Almudena Clavijo a, Isidoro A. Gómez a a b
Departamento de Cristalografía, Mineralogía y Química Agrícola, Facultad de Química, Universidad de Sevilla, c/Profesor García González 1, 41012 Sevilla, Spain Distrito Sanitario Guadalquivir, Servicio Andaluz de Salud, Junta de Andalucía, Avda. de los Aguijones s/n, 14011 Córdoba, Spain
a r t i c l e
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Article history: Received 2 October 2009 Received in revised form 29 October 2009 Accepted 29 October 2009 Available online 13 November 2009 Keywords: Wine classification Metals Linear discriminant analysis Pattern recognition methods Spain
a b s t r a c t Young and aged wines from two viticole zones in the Andalusian province of Córdoba (southern Spain) were analysed for their content in Ca, Mg, Fe, Cu, Mn and Zn by flame atomic absorption spectrophotometry, and Na, K, Al and Sr by flame atomic emission spectrophotometry. Significant differences in mean content were found for Na, Mn, Mg, Fe and Zn between wines from Montilla–Moriles and Villaviciosa. Linear discriminant analysis using those variables gave 97.9% recognition ability and 95.7% prediction ability. Cluster and principal component analysis show some differences in wines according to geographical origin and to the ageing of wines. Significant differences between young and aged wines were found in the mean content for Mg, K, Sr, Zn and Mn, obtaining 93.62% recognition ability and prediction ability by using linear discriminant analysis and leave-one-out cross-validation test, respectively. Finally, linear discriminant analysis could also be able to classify the samples according to their provenance and to their ageing simultaneously, obtaining 93.6% of the wines correctly classified. © 2009 Elsevier B.V. All rights reserved.
1. Introduction The composition of wine is due to many factors related to the specific production area, such as grape variety, soil and climate, culture, yeast, wine making practices, transport and storage. All of them have an important influence on the quality of wine, and they are very important in the characterisation and differentiation of wines [1,2]. Different physicochemical parameters such as volatile organic compounds [3,4] and combinations of different kinds of physicochemical parameters have been used with this objective: phenolic compounds, metals, chromatic and general enological parameters to classify Spanish DO rose wines [5]; anthocyanins and non-coloured phenolic compounds, minerals and sensory analysis were used to classify Greek wines [6]; and metals together with volatile and phenolic compounds in the characterisation of Galician (NW Spain) Ribeira Sacra wines [7]. The amount of inorganic ions in wine is of great interest, because of their influence on wine technology as well as their toxic effects [8]. However, one of the main interests is to use the mineral content to characterise the wines by their geographical origin taking into account the relationship between the metallic content in the samples and soil composition. This differentiation can be carried out by using major, trace and ultratrace elements. Major elements have been widely used for differentiating Spanish wines according to their
⁎ Corresponding author. Tel.: +34 954447137; fax: +34 954557140. E-mail address: [email protected] (P. Paneque). 0026-265X/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.microc.2009.10.017
provenance [9–13]. Finally, the content of some inorganic anions and heavy metal has been also correlated with some other characteristics of the wines such as the age, the colour and the sugar content [14]. Montilla–Moriles DO is located some 50 km south of Córdoba (Spain). Montilla is the main production centre of this DO. Most of the Montilla wines are produced with the Pedro Ximénez variety. Typical vineyard soils are “albero” and “albariza”. These types of soil correspond to the upper limit of the Pliocene period, which is characterized by the presence of white marls (argile-containing limestone) typical of the Guadalquivir basin. The climate of Montilla– Moriles zone is mainly Mediterranean but with some continental features due to altitude and location. Wines produced in this DO are white young wines and sherry type wines — such as fino, oloroso, amontillado and Pedro Ximenez — produced according to the dynamic ageing system formerly called the “Soleras and Criaderas” method. Villaviciosa de Córdoba is located 48 km north of Córdoba. Together with the neighbouring locality of Espiel it constitutes the viticole area of Villaviciosa, whose wines have recently received the apellation of Vinos de la Tierra de Villaviciosa de Córdoba [15]. Grape varieties employed for the vinification are Pedro Ximénez, Palomino Fino, Airén, Moscatel de Alejandría, among others. Wines from this apellation include white young wines, white aged wines (biological and oxidative ageing) produced by the previously cited “Soleras and Criaderas” method, and sweet wines. In this paper, Ca, Mg, Fe, Cu, Mn and Zn have been determined by flame atomic absorption spectrophotometry, and Na, K, Al and Sr by flame atomic emission spectrophotometry in samples of young and aged wines of the province of Córdoba (southern Spain) from two
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different wine producing areas, Montilla–Moriles DO and Vinos de la Tierra de Villaviciosa de Córdoba apellation. Those elements were used as chemical descriptors to apply methods like non-supervised pattern recognition techniques such as principal component analysis (PCA) and cluster analysis (CA) and supervised techniques like linear discriminant analysis (LDA). These metallic descriptors have been previously used for the differentiation of fino and oloroso sherry wines from different Andalusian denominations of origin [9,13], and for the study of the mineral profile of Montilla–Moriles fino wines [16]. 2. Materials and methods 2.1. Apparatus A Perkin Elmer AAnalyst 100 (Norwalk, CT, USA) atomic absorption spectrometer was used for metal determination involving atomic absorption spectrometry (AAS) and atomic emission spectrometry (AES). Table 1 resumes the instrumental conditions for the determination of each element. 2.2. Reagents Panreac (Barcelona, Spain) AA standard solutions of about 1000 mg L− 1 were used as stock solution for calibration. Other reagents were of analytical grade. Milli-Q treated water was used throughout. 2.3. Samples Twenty six samples of white wines of the zone with DO Montilla– Moriles (code MM) and 21 samples of white wines with the apellation Vinos de la Tierra de Villaviciosa (code VV) were purchased in markets or in the cellars. Samples corresponded to different types of wines: young wines (15 and 7 samples in MM and VV, respectively) and aged wines (11 and 14 samples in MM and VV, respectively). Each sample was identified with a code referring its origin class (MM, VV) together with the sample number. The containers used for storing and treating the samples were cleaned to avoid contamination of the samples with traces of any metal. Containers were treated with chromic acid mixture followed with two washes with milli-Q water. Once opened, wine samples were digested according to the following procedure: 25 mL of wines with 15 mL of hydrogen peroxide are heated at 80 °C until a volume of about 20 mL. Then 1 mL of nitric acid is added, and the heating is continued until a final volume of about 2 mL. This volume is mixed with milli-Q treated water up to the starting volume. Three replicated digestions were made for each sample. Hence, from each wine sample (bottle), three equivalent aliquots were digested and analysed. The triplicates give us insight about differences in sample treatment and response, but not about sample variability, because any of the aliquots were drawn of the same bottle and the wine samples were assumed to be homogeneous. Accordingly
Table 1 Instrumental conditions for the determination of each element. Element
Wavelenght (nm)
Type of flame
Ca Mg Na K Fe Cu Mn Zn Al Sr
422.7 285.2 589.0 760.5 248.3 324.8 279.5 213.9 309.3 460.7
Air/acetylene Air/acetylene Air/acetylene Air/acetylene Air/acetylene Air/acetylene Air/acetylene Air/acetylene Acetylene/nitrous oxide Acetylene/nitrous oxide
Table 2 Metal concentration in Montilla–Moriles samples (n = 26 samples) and Villaviciosa samples (n = 21 samples). Element
Ca Mg Na K Fe Cu Mn Zn Al Sr
Montilla–Moriles samples
Villaviciosa samples
Mean ± SD (mg L− 1)
Range of quantified values (mg L− 1)
Mean ± SD (mg L− 1)
Range of quantified values (mg L− 1)
77.9 ± 18.0 90.8 ± 37.5 46.6 ± 12.5 849 ± 301 3.49 ± 2.21 0.47 ± 0.33 0.98 ± 0.40 0.79 ± 1.01 1.92 ± 1.00 0.87 ± 0.41
45.3–111.0 54.7–158.2 30.4–84.2 303–1330 1.07–9.83 0.10–1.58 0.43–1.65 0.07–4.43 0.40–3.46 0.26–1.93
71.2 ± 21.5 110.8 ± 21.5 21.8 ± 10.1 837.3 ± 238.2 6.31 ± 3.21 0.39 ± 0.74 1.66 ± 0.34 0.82 ± 0.81 1.10 ± 0.54 0.72 ± 0.34
40.8–111.8 80.3–154.3 9.3–49.8 605–1605 2.20–12.8 0.04–3.31 1.09–2.56 0.14–3.40 0.26–1.95 0.39–1.19
we have used the average value from the triplicate as a central measurement for the metal content of the sample. Metals Ca, Mg, Fe, Cu, Mn and Zn were determined from AAS, and Na and K were measured from AES, using an air/acetylene flame. Al and Sr were determined from AES using acetylene/nitrous oxide flame. 2.4. Statistical analysis Univariate and multivariate analysis were carried out by using the STATISTICA 7 package of Stafsoft [17]. A data matrix was built, consisting of 10 columns (the analysed elements) and 47 rows (the samples of the two categories, geographical origin). The statistical analyses were firstly performed by both grouping the wines according to their provenance — (MM) Montilla–Moriles wines, and (VV)
Table 3 Metal concentration in young (n = 22 samples) and aged (n = 25 samples) wines from Montilla–Moriles and Villaviciosa. Element
Ca Mg Na K Fe Cu Mn Zn Al Sr
Young samples
Aged samples
Mean ± SD (mg L− 1)
Range of quantified values (mg L−1)
Mean ± SD (mg L− 1)
Range of quantified values (mg L− 1)
64.1 ± 16.1 73.5 ± 14.5 33.2 ± 16.1 656 ± 189 4.73 ± 3.49 0.22 ± 0.18 1.02 ± 0.37 0.37 ± 0.22 1.18 ± 0.88 0.60 ± 0.24
40.8–92.8 54.7–100.0 9.3–66.4 303–1033 1.07–12.85 0.04–0.69 0.51–1.60 0.07–0.97 0.28–3.46 0.26–1.03
88.4 ± 17.7 122.8 ± 26.0 37.6 ± 17.6 1010 ± 224 4.77 ± 2.62 0.62 ± 0.68 1.52 ± 0.49 1.19 ± 1.11 1.88 ± 0.83 0.98 ± 0.32
43.3–111.8 74.2–158.2 14.4–84.2 613–1605 1.34–12.25 0.09–3.31 0.43–2.56 0.13–4.43 0.26–3.32 0.51–1.93
Table 4 Metal concentration in Montilla–Moriles (MM) and Villaviciosa (VV) samples considering the elaboration type (young (J) and aged (C) wines) (MMJ, n = 15 samples; MMC, n = 11 samples; VVJ, n = 7 samples; VVC, n = 14 samples). Element
Montilla–Moriles samples
Villaviciosa samples
MMJ
VVJ
MMC −1
Mean ± SD (mg L Ca Mg Na K Fe Cu Mn Zn Al Sr
68.3 ± 16.2 65.9 ± 9.4 42.4 ± 9.8 656 ± 228 3.51 ± 2.69 0.29 ± 0.17 0.86 ± 0.34 0.42 ± 0.23 1.45 ± 0.93 0.65 ± 0.28
)
90.9 ± 11.0 124.7 ± 34.6 52.4 ± 13.9 1112 ± 150 3.46 ± 1.44 0.71 ± 0.34 1.15 ± 0.42 1.30 ± 1.40 2.56 ± 0.71 1.16 ± 0.37
VVC −1
Mean ± SD (mg L 54.9 ± 12.3 89.7 ± 8.8 13.5 ± 4.1 654 ± 43 7.34 ± 3.75 0.06 ± 0.03 1.36 ± 0.12 0.26 ± 0.15 0.59 ± 0.29 0.48 ± 0.07
)
79.4 ± 20.5 121.3 ± 17.8 26.0 ± 9.6 929 ± 243 5.80 ± 2.92 0.55 ± 0.87 1.80 ± 0.32 1.10 ± 0.86 1.35 ± 0.46 0.84 ± 0.20
P. Paneque et al. / Microchemical Journal 94 (2010) 175–179
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Fig. 1. Principal component scatter plot of young (J) and aged (C) wine samples of Montilla–Moriles (MM) and Villaviciosa (VV).
Villaviciosa wines — and according to their possible ageing — (J) young wines, and (C) aged wines. Finally, four classes were considered taking into account both provenance and ageing: (MMJ) Montilla–Moriles young wines, (MMC) Montilla–Moriles aged wines, (VVJ) Villaviciosa young wines and (MMC) Villaviciosa aged wines.
3. Results and discussion 3.1. Mineral content The metal content of forty seven wines from Córdoba was determined. The results, expressed in milligram per litre always, were the average of triplicate measurements. The corresponding descriptive basic statistic considering the geographical origin (MM, VV), the type of elaboration (young (J) or aged (C) wines) and both factors simultaneously (MMJ, MMC, VVJ, VVC) are shown in Tables 2–4, respectively. The mean content for the majority of these metals in the wines analysed is consistent with most of the values described in the literature by other authors [18–21].
3.2. Effect of the geographical origin of the samples A Wald–Wolfowitz non-parametric test was performed in order to establish the discriminant capacity of each variable, using the geographical origin as a category. Results indicated that sodium, magnesium, manganesum, iron and zinc are the most discriminant variables. Therefore, a rough feature selection, based on the Coomans' weight (g) [22] was performed, and sodium and manganesum were found to be the most important features to discriminate either MM or VV categories. The metal profile of wines has been used as chemical descriptors for their classification according to geographical origin. For multivariate analysis only those variables which showed some differences between the categories were used; in our case, Na, Mg, Mn, Fe and Zn. Although our research begins from the a priori knowledge of the class membership of wines, principal components analysis (PCA) and cluster analysis (CA) have been applied. As for the PCA results, Fig. 1 shows the score plot of the first two principal components, that account for 74.8% of the total variance. A rough clustering of wines can be observed according to different geographical origins and types of wine (young or
Fig. 2. Dendrogram of cluster anlaysis of wine samples of Montilla–Moriles (MM) and Villaviciosa (VV). J and C letters at the end of the code, indicate young and aged wines, respectively.
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Table 5 Classification of the samples in the two geographical zones (MM and VV) and recognition and prediction abilities using sodium, magnesium, manganesum, iron and zinc, and LDA technique and leave-one-out cross-validation technique, respectively.
Montilla–Moriles (MM) Villaviciosa (VV) Total
Recognition ability (%)
MM
VV
100.0 95.2 97.9
26 1
0 20
Prediction ability (%)
MM
VV
25 1
1 20
Montilla–Moriles (MM) Villaviciosa (VV) Total
96.2 95.2 95.7
Table 6 Classification of the samples according to type of elaboration (young (J) and aged (C) wines) and recognition and prediction abilities using magnesium, potassium, strontium, manganesum and zinc, and LDA technique and Leave-one-out crossvalidation technique, respectively.
Young wines (J) Aged wines (C) Total
Young wines (J) Aged wines (C) Total
Recognition ability (%)
J
C
100.0 88.0 93.6
22 3
0 22
Prediction ability (%)
J
C
100.0 88.0 93.6
22 3
0 22
aged wine). On the first principal component, aged wines (to the right of the graph) are roughly separated from young wine samples, to the left; and on the second principal component, samples of Montilla–Moriles DO (MM) have higher scores than the wines from Villaviciosa (VV) apellation. According to the loading for each variable in the two PCs, magnesium, manganesun and zinc are the most important variables in the first PC thus differentiating by the type of wine, while sodium is the most important variable in the second PC, related to provenance, which is consistent with Cooman's weight study. As cited by [5], edaphical conditions of each Denomination of Origin (or apellation) influence the levels of metals such as the sodium, among others, and then, the wines. Regarding to CA, Fig. 2 sets out the dendrogram obtained from using Ward's hierarchical method and city's block (Manhattan) distance as a criterion of similarity. Two main clusters can be observed: the first one constituted of 14 samples of MM and VV categories corresponding to aged wines, seven samples of each. In the
Table 7 Classification of the samples in the four categories (MMJ, MMC, VVJ and VVC) and recognition ability using ten variables and LDA technique.
Montilla–Moriles young wines (MMJ) Montilla–Moriles aged wines (MMC) Villaviciosa young wines (VVJ) Villaviciosa aged wines (VVC) Total
Recognition ability (%)
MMJ
MMC
VVJ
VVC
100 90.9 100 87.7 93.6
15 1 – –
– 10 – –
– – 7 2
– – – 12
second cluster, two subclusters can be observed, the first for MM category samples, and the second one mainly constituted of VV samples. Accordingly, it seems that the variables used have sufficient explanatory power to detect not only the geographical origin but also the type of wine according to ageing. LDA is a widespread parametric method for classification purposes that assumes an a priori knowledge of the number of classes and the sample class membership. The classification was performed according to the geographical origin: Montilla–Moriles (MM) and Villaviciosa (VV). The variables selected were sodium, magnesiun, manganesum, iron and zinc. The recognition ability is 100% for MM category and 95.2% for VV category, with a single incorrected classified sample (Table 5). Validation of these results was performed using the leaveone-out cross-validation test. During this test, a sample is removed from the data set. The classification model is rebuilt and the removed sample is classified in this new model. All the samples were sequentially removed and reclassified. The classifications obtained are presented in the Table 5. The total prediction ability is 95.7%. 3.3. Effect of the type of elaboration When the type of elaboration was considered (young wines and aged wines), Wald–Wolfowitz test showed significant differences in magnesium, potassium, strontium, zinc and manganesum contents. According to Coomans' weight, Mg is the most discriminant variable for differentiating between young and aged wines. LDA analysis using Mg, K, Sr, Zn and Mn variables revealed good classification of the samples according to the type of elaboration (100% for young wines and 88% for aged wines) (Table 6). Leave-one-out cross-validation test obtained the same results (93.6% total prediction ability) (Table 6). 3.4. Effect of the geographical origin and type of wine PCA and CA showed a natural grouping of the samples according to their geographical origin but also to the ageing of the wines (Figs. 1
Fig. 3. Discriminant scatter plot of young (J) and aged (C) wine samples of Montilla–Moriles (MM) and Villaviciosa (VV).
P. Paneque et al. / Microchemical Journal 94 (2010) 175–179 Table 8 Classification of the samples in the four categories (MMJ, MMC, VVJ and VVC) and prediction ability using ten variables and leave-one-out cross-validation technique.
Montilla–Moriles young wines (MMJ) Montilla–Moriles aged wines (MMC) Villaviciosa young wines (VVJ) Villaviciosa aged wines (VVC) Total
Recognition ability (%)
MMJ
MMC
VVJ
VVC
80.0 81.8 71.4 71.4 76.2
12 2 0 0
3 9 0 0
0 0 5 4
0 0 2 10
179
Accordingly, unsupervised and supervised pattern recognition methods could distinguish Cordoban wine samples when considering their provenance and their type of elaboration (with or without ageing). Acknowledgement We express our gratitude to Villaviciosa wineries which enabled us to use samples of precisely known origin and to A.G. González for his critical review of the paper. References
and 2). Accordingly, it seems interesting to perform a discriminant analysis using the origin and the ageing of the samples as a factor for the classification. Four categories were considered, MMC and MMJ, for aged and young wines from Montilla–Moriles, and VVC and VVJ, for aged and young wines from Villaviciosa, respectively. Kruskall–Wallis test revealed differences in the content of all the mineral elements analysed, and Tukey post-hoc test indicated which variables differentiated between the four categories considered. As indicated before, Na clearly differentiates between wines from Montilla–Moriles and from Villaviciosa, and Mg differentiates between young and aged wines. The performance of LDA obtained three statistically significant discriminant functions, with sodium, zinc, potassium, strontium and cupper in the first function, magnesium, aluminum and calcium in the second and manganesum and iron in the third as the most important variables. The plot of functions one and two is represented in Fig. 3, where a good differentiation between MM and VV categories can be visualised according to function 1, and aged wines categories (MMC and VVC) can be well differentiated from MMJ young wines according to function 2. Accordingly, sodium contributes to differentiate between wines from MM and VV classes; and magnesium discriminates aged wines from young wines. The total recognition ability was 93.6% according to the four categories considerated (Table 7). Validation of these results was performed using leave-one-out cross-validation. Table 8 presents the classifications obtained for each category. It is interesting to remark that wine samples which were incorrectly classified, were classified in the category corresponding to their provenance. Thus, the model cannot differentiate —in those cases — the type of wine but the geographical origin of the wines. The total prediction ability is 76.2%. 4. Conclusions Ten elements were used to characterise southern Spanish wines from Montilla–Moriles DO and Vinos de la Tierra de Villaviciosa de Córdoba apellation. The geographical origin and the type of wines influence the final metallic contents. Thus, univariate analysis showed that wines of Montilla–Moriles can be differentiated from Villaviciosa wines mainly according to the content in sodium, manganesum, magnesium, iron and zinc. LDA showed good recognition and prediction abilities using these five mineral elements. On the other hand, young wines can be differentiated from aged wines for their content in magnesium, potassium, strontium, zinc and manganesun. Young wines were 100% classified in their category whereas aged wines were 88% when applying LDA analysis. PCA and CA showed that wine samples could be roughly grouped in accordance with their geographical origin and to their ageing. Thus, statistical differences were found in all the elements analysed when considering four sample categories according to the origin and the type of wine (young and aged wines). Prediction ability was 100% for young wines from Montilla–Moriles and Villaviciosa, and close to 90% for aged wines.
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Microchemical Journal j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / m i c r o c
Metal content in southern Spain wines and their classification according to origin and ageing Patricia Paneque a,⁎, Ma Teresa Álvarez-Sotomayor b, Almudena Clavijo a, Isidoro A. Gómez a a b
Departamento de Cristalografía, Mineralogía y Química Agrícola, Facultad de Química, Universidad de Sevilla, c/Profesor García González 1, 41012 Sevilla, Spain Distrito Sanitario Guadalquivir, Servicio Andaluz de Salud, Junta de Andalucía, Avda. de los Aguijones s/n, 14011 Córdoba, Spain
a r t i c l e
i n f o
Article history: Received 2 October 2009 Received in revised form 29 October 2009 Accepted 29 October 2009 Available online 13 November 2009 Keywords: Wine classification Metals Linear discriminant analysis Pattern recognition methods Spain
a b s t r a c t Young and aged wines from two viticole zones in the Andalusian province of Córdoba (southern Spain) were analysed for their content in Ca, Mg, Fe, Cu, Mn and Zn by flame atomic absorption spectrophotometry, and Na, K, Al and Sr by flame atomic emission spectrophotometry. Significant differences in mean content were found for Na, Mn, Mg, Fe and Zn between wines from Montilla–Moriles and Villaviciosa. Linear discriminant analysis using those variables gave 97.9% recognition ability and 95.7% prediction ability. Cluster and principal component analysis show some differences in wines according to geographical origin and to the ageing of wines. Significant differences between young and aged wines were found in the mean content for Mg, K, Sr, Zn and Mn, obtaining 93.62% recognition ability and prediction ability by using linear discriminant analysis and leave-one-out cross-validation test, respectively. Finally, linear discriminant analysis could also be able to classify the samples according to their provenance and to their ageing simultaneously, obtaining 93.6% of the wines correctly classified. © 2009 Elsevier B.V. All rights reserved.
1. Introduction The composition of wine is due to many factors related to the specific production area, such as grape variety, soil and climate, culture, yeast, wine making practices, transport and storage. All of them have an important influence on the quality of wine, and they are very important in the characterisation and differentiation of wines [1,2]. Different physicochemical parameters such as volatile organic compounds [3,4] and combinations of different kinds of physicochemical parameters have been used with this objective: phenolic compounds, metals, chromatic and general enological parameters to classify Spanish DO rose wines [5]; anthocyanins and non-coloured phenolic compounds, minerals and sensory analysis were used to classify Greek wines [6]; and metals together with volatile and phenolic compounds in the characterisation of Galician (NW Spain) Ribeira Sacra wines [7]. The amount of inorganic ions in wine is of great interest, because of their influence on wine technology as well as their toxic effects [8]. However, one of the main interests is to use the mineral content to characterise the wines by their geographical origin taking into account the relationship between the metallic content in the samples and soil composition. This differentiation can be carried out by using major, trace and ultratrace elements. Major elements have been widely used for differentiating Spanish wines according to their
⁎ Corresponding author. Tel.: +34 954447137; fax: +34 954557140. E-mail address: [email protected] (P. Paneque). 0026-265X/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.microc.2009.10.017
provenance [9–13]. Finally, the content of some inorganic anions and heavy metal has been also correlated with some other characteristics of the wines such as the age, the colour and the sugar content [14]. Montilla–Moriles DO is located some 50 km south of Córdoba (Spain). Montilla is the main production centre of this DO. Most of the Montilla wines are produced with the Pedro Ximénez variety. Typical vineyard soils are “albero” and “albariza”. These types of soil correspond to the upper limit of the Pliocene period, which is characterized by the presence of white marls (argile-containing limestone) typical of the Guadalquivir basin. The climate of Montilla– Moriles zone is mainly Mediterranean but with some continental features due to altitude and location. Wines produced in this DO are white young wines and sherry type wines — such as fino, oloroso, amontillado and Pedro Ximenez — produced according to the dynamic ageing system formerly called the “Soleras and Criaderas” method. Villaviciosa de Córdoba is located 48 km north of Córdoba. Together with the neighbouring locality of Espiel it constitutes the viticole area of Villaviciosa, whose wines have recently received the apellation of Vinos de la Tierra de Villaviciosa de Córdoba [15]. Grape varieties employed for the vinification are Pedro Ximénez, Palomino Fino, Airén, Moscatel de Alejandría, among others. Wines from this apellation include white young wines, white aged wines (biological and oxidative ageing) produced by the previously cited “Soleras and Criaderas” method, and sweet wines. In this paper, Ca, Mg, Fe, Cu, Mn and Zn have been determined by flame atomic absorption spectrophotometry, and Na, K, Al and Sr by flame atomic emission spectrophotometry in samples of young and aged wines of the province of Córdoba (southern Spain) from two
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different wine producing areas, Montilla–Moriles DO and Vinos de la Tierra de Villaviciosa de Córdoba apellation. Those elements were used as chemical descriptors to apply methods like non-supervised pattern recognition techniques such as principal component analysis (PCA) and cluster analysis (CA) and supervised techniques like linear discriminant analysis (LDA). These metallic descriptors have been previously used for the differentiation of fino and oloroso sherry wines from different Andalusian denominations of origin [9,13], and for the study of the mineral profile of Montilla–Moriles fino wines [16]. 2. Materials and methods 2.1. Apparatus A Perkin Elmer AAnalyst 100 (Norwalk, CT, USA) atomic absorption spectrometer was used for metal determination involving atomic absorption spectrometry (AAS) and atomic emission spectrometry (AES). Table 1 resumes the instrumental conditions for the determination of each element. 2.2. Reagents Panreac (Barcelona, Spain) AA standard solutions of about 1000 mg L− 1 were used as stock solution for calibration. Other reagents were of analytical grade. Milli-Q treated water was used throughout. 2.3. Samples Twenty six samples of white wines of the zone with DO Montilla– Moriles (code MM) and 21 samples of white wines with the apellation Vinos de la Tierra de Villaviciosa (code VV) were purchased in markets or in the cellars. Samples corresponded to different types of wines: young wines (15 and 7 samples in MM and VV, respectively) and aged wines (11 and 14 samples in MM and VV, respectively). Each sample was identified with a code referring its origin class (MM, VV) together with the sample number. The containers used for storing and treating the samples were cleaned to avoid contamination of the samples with traces of any metal. Containers were treated with chromic acid mixture followed with two washes with milli-Q water. Once opened, wine samples were digested according to the following procedure: 25 mL of wines with 15 mL of hydrogen peroxide are heated at 80 °C until a volume of about 20 mL. Then 1 mL of nitric acid is added, and the heating is continued until a final volume of about 2 mL. This volume is mixed with milli-Q treated water up to the starting volume. Three replicated digestions were made for each sample. Hence, from each wine sample (bottle), three equivalent aliquots were digested and analysed. The triplicates give us insight about differences in sample treatment and response, but not about sample variability, because any of the aliquots were drawn of the same bottle and the wine samples were assumed to be homogeneous. Accordingly
Table 1 Instrumental conditions for the determination of each element. Element
Wavelenght (nm)
Type of flame
Ca Mg Na K Fe Cu Mn Zn Al Sr
422.7 285.2 589.0 760.5 248.3 324.8 279.5 213.9 309.3 460.7
Air/acetylene Air/acetylene Air/acetylene Air/acetylene Air/acetylene Air/acetylene Air/acetylene Air/acetylene Acetylene/nitrous oxide Acetylene/nitrous oxide
Table 2 Metal concentration in Montilla–Moriles samples (n = 26 samples) and Villaviciosa samples (n = 21 samples). Element
Ca Mg Na K Fe Cu Mn Zn Al Sr
Montilla–Moriles samples
Villaviciosa samples
Mean ± SD (mg L− 1)
Range of quantified values (mg L− 1)
Mean ± SD (mg L− 1)
Range of quantified values (mg L− 1)
77.9 ± 18.0 90.8 ± 37.5 46.6 ± 12.5 849 ± 301 3.49 ± 2.21 0.47 ± 0.33 0.98 ± 0.40 0.79 ± 1.01 1.92 ± 1.00 0.87 ± 0.41
45.3–111.0 54.7–158.2 30.4–84.2 303–1330 1.07–9.83 0.10–1.58 0.43–1.65 0.07–4.43 0.40–3.46 0.26–1.93
71.2 ± 21.5 110.8 ± 21.5 21.8 ± 10.1 837.3 ± 238.2 6.31 ± 3.21 0.39 ± 0.74 1.66 ± 0.34 0.82 ± 0.81 1.10 ± 0.54 0.72 ± 0.34
40.8–111.8 80.3–154.3 9.3–49.8 605–1605 2.20–12.8 0.04–3.31 1.09–2.56 0.14–3.40 0.26–1.95 0.39–1.19
we have used the average value from the triplicate as a central measurement for the metal content of the sample. Metals Ca, Mg, Fe, Cu, Mn and Zn were determined from AAS, and Na and K were measured from AES, using an air/acetylene flame. Al and Sr were determined from AES using acetylene/nitrous oxide flame. 2.4. Statistical analysis Univariate and multivariate analysis were carried out by using the STATISTICA 7 package of Stafsoft [17]. A data matrix was built, consisting of 10 columns (the analysed elements) and 47 rows (the samples of the two categories, geographical origin). The statistical analyses were firstly performed by both grouping the wines according to their provenance — (MM) Montilla–Moriles wines, and (VV)
Table 3 Metal concentration in young (n = 22 samples) and aged (n = 25 samples) wines from Montilla–Moriles and Villaviciosa. Element
Ca Mg Na K Fe Cu Mn Zn Al Sr
Young samples
Aged samples
Mean ± SD (mg L− 1)
Range of quantified values (mg L−1)
Mean ± SD (mg L− 1)
Range of quantified values (mg L− 1)
64.1 ± 16.1 73.5 ± 14.5 33.2 ± 16.1 656 ± 189 4.73 ± 3.49 0.22 ± 0.18 1.02 ± 0.37 0.37 ± 0.22 1.18 ± 0.88 0.60 ± 0.24
40.8–92.8 54.7–100.0 9.3–66.4 303–1033 1.07–12.85 0.04–0.69 0.51–1.60 0.07–0.97 0.28–3.46 0.26–1.03
88.4 ± 17.7 122.8 ± 26.0 37.6 ± 17.6 1010 ± 224 4.77 ± 2.62 0.62 ± 0.68 1.52 ± 0.49 1.19 ± 1.11 1.88 ± 0.83 0.98 ± 0.32
43.3–111.8 74.2–158.2 14.4–84.2 613–1605 1.34–12.25 0.09–3.31 0.43–2.56 0.13–4.43 0.26–3.32 0.51–1.93
Table 4 Metal concentration in Montilla–Moriles (MM) and Villaviciosa (VV) samples considering the elaboration type (young (J) and aged (C) wines) (MMJ, n = 15 samples; MMC, n = 11 samples; VVJ, n = 7 samples; VVC, n = 14 samples). Element
Montilla–Moriles samples
Villaviciosa samples
MMJ
VVJ
MMC −1
Mean ± SD (mg L Ca Mg Na K Fe Cu Mn Zn Al Sr
68.3 ± 16.2 65.9 ± 9.4 42.4 ± 9.8 656 ± 228 3.51 ± 2.69 0.29 ± 0.17 0.86 ± 0.34 0.42 ± 0.23 1.45 ± 0.93 0.65 ± 0.28
)
90.9 ± 11.0 124.7 ± 34.6 52.4 ± 13.9 1112 ± 150 3.46 ± 1.44 0.71 ± 0.34 1.15 ± 0.42 1.30 ± 1.40 2.56 ± 0.71 1.16 ± 0.37
VVC −1
Mean ± SD (mg L 54.9 ± 12.3 89.7 ± 8.8 13.5 ± 4.1 654 ± 43 7.34 ± 3.75 0.06 ± 0.03 1.36 ± 0.12 0.26 ± 0.15 0.59 ± 0.29 0.48 ± 0.07
)
79.4 ± 20.5 121.3 ± 17.8 26.0 ± 9.6 929 ± 243 5.80 ± 2.92 0.55 ± 0.87 1.80 ± 0.32 1.10 ± 0.86 1.35 ± 0.46 0.84 ± 0.20
P. Paneque et al. / Microchemical Journal 94 (2010) 175–179
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Fig. 1. Principal component scatter plot of young (J) and aged (C) wine samples of Montilla–Moriles (MM) and Villaviciosa (VV).
Villaviciosa wines — and according to their possible ageing — (J) young wines, and (C) aged wines. Finally, four classes were considered taking into account both provenance and ageing: (MMJ) Montilla–Moriles young wines, (MMC) Montilla–Moriles aged wines, (VVJ) Villaviciosa young wines and (MMC) Villaviciosa aged wines.
3. Results and discussion 3.1. Mineral content The metal content of forty seven wines from Córdoba was determined. The results, expressed in milligram per litre always, were the average of triplicate measurements. The corresponding descriptive basic statistic considering the geographical origin (MM, VV), the type of elaboration (young (J) or aged (C) wines) and both factors simultaneously (MMJ, MMC, VVJ, VVC) are shown in Tables 2–4, respectively. The mean content for the majority of these metals in the wines analysed is consistent with most of the values described in the literature by other authors [18–21].
3.2. Effect of the geographical origin of the samples A Wald–Wolfowitz non-parametric test was performed in order to establish the discriminant capacity of each variable, using the geographical origin as a category. Results indicated that sodium, magnesium, manganesum, iron and zinc are the most discriminant variables. Therefore, a rough feature selection, based on the Coomans' weight (g) [22] was performed, and sodium and manganesum were found to be the most important features to discriminate either MM or VV categories. The metal profile of wines has been used as chemical descriptors for their classification according to geographical origin. For multivariate analysis only those variables which showed some differences between the categories were used; in our case, Na, Mg, Mn, Fe and Zn. Although our research begins from the a priori knowledge of the class membership of wines, principal components analysis (PCA) and cluster analysis (CA) have been applied. As for the PCA results, Fig. 1 shows the score plot of the first two principal components, that account for 74.8% of the total variance. A rough clustering of wines can be observed according to different geographical origins and types of wine (young or
Fig. 2. Dendrogram of cluster anlaysis of wine samples of Montilla–Moriles (MM) and Villaviciosa (VV). J and C letters at the end of the code, indicate young and aged wines, respectively.
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Table 5 Classification of the samples in the two geographical zones (MM and VV) and recognition and prediction abilities using sodium, magnesium, manganesum, iron and zinc, and LDA technique and leave-one-out cross-validation technique, respectively.
Montilla–Moriles (MM) Villaviciosa (VV) Total
Recognition ability (%)
MM
VV
100.0 95.2 97.9
26 1
0 20
Prediction ability (%)
MM
VV
25 1
1 20
Montilla–Moriles (MM) Villaviciosa (VV) Total
96.2 95.2 95.7
Table 6 Classification of the samples according to type of elaboration (young (J) and aged (C) wines) and recognition and prediction abilities using magnesium, potassium, strontium, manganesum and zinc, and LDA technique and Leave-one-out crossvalidation technique, respectively.
Young wines (J) Aged wines (C) Total
Young wines (J) Aged wines (C) Total
Recognition ability (%)
J
C
100.0 88.0 93.6
22 3
0 22
Prediction ability (%)
J
C
100.0 88.0 93.6
22 3
0 22
aged wine). On the first principal component, aged wines (to the right of the graph) are roughly separated from young wine samples, to the left; and on the second principal component, samples of Montilla–Moriles DO (MM) have higher scores than the wines from Villaviciosa (VV) apellation. According to the loading for each variable in the two PCs, magnesium, manganesun and zinc are the most important variables in the first PC thus differentiating by the type of wine, while sodium is the most important variable in the second PC, related to provenance, which is consistent with Cooman's weight study. As cited by [5], edaphical conditions of each Denomination of Origin (or apellation) influence the levels of metals such as the sodium, among others, and then, the wines. Regarding to CA, Fig. 2 sets out the dendrogram obtained from using Ward's hierarchical method and city's block (Manhattan) distance as a criterion of similarity. Two main clusters can be observed: the first one constituted of 14 samples of MM and VV categories corresponding to aged wines, seven samples of each. In the
Table 7 Classification of the samples in the four categories (MMJ, MMC, VVJ and VVC) and recognition ability using ten variables and LDA technique.
Montilla–Moriles young wines (MMJ) Montilla–Moriles aged wines (MMC) Villaviciosa young wines (VVJ) Villaviciosa aged wines (VVC) Total
Recognition ability (%)
MMJ
MMC
VVJ
VVC
100 90.9 100 87.7 93.6
15 1 – –
– 10 – –
– – 7 2
– – – 12
second cluster, two subclusters can be observed, the first for MM category samples, and the second one mainly constituted of VV samples. Accordingly, it seems that the variables used have sufficient explanatory power to detect not only the geographical origin but also the type of wine according to ageing. LDA is a widespread parametric method for classification purposes that assumes an a priori knowledge of the number of classes and the sample class membership. The classification was performed according to the geographical origin: Montilla–Moriles (MM) and Villaviciosa (VV). The variables selected were sodium, magnesiun, manganesum, iron and zinc. The recognition ability is 100% for MM category and 95.2% for VV category, with a single incorrected classified sample (Table 5). Validation of these results was performed using the leaveone-out cross-validation test. During this test, a sample is removed from the data set. The classification model is rebuilt and the removed sample is classified in this new model. All the samples were sequentially removed and reclassified. The classifications obtained are presented in the Table 5. The total prediction ability is 95.7%. 3.3. Effect of the type of elaboration When the type of elaboration was considered (young wines and aged wines), Wald–Wolfowitz test showed significant differences in magnesium, potassium, strontium, zinc and manganesum contents. According to Coomans' weight, Mg is the most discriminant variable for differentiating between young and aged wines. LDA analysis using Mg, K, Sr, Zn and Mn variables revealed good classification of the samples according to the type of elaboration (100% for young wines and 88% for aged wines) (Table 6). Leave-one-out cross-validation test obtained the same results (93.6% total prediction ability) (Table 6). 3.4. Effect of the geographical origin and type of wine PCA and CA showed a natural grouping of the samples according to their geographical origin but also to the ageing of the wines (Figs. 1
Fig. 3. Discriminant scatter plot of young (J) and aged (C) wine samples of Montilla–Moriles (MM) and Villaviciosa (VV).
P. Paneque et al. / Microchemical Journal 94 (2010) 175–179 Table 8 Classification of the samples in the four categories (MMJ, MMC, VVJ and VVC) and prediction ability using ten variables and leave-one-out cross-validation technique.
Montilla–Moriles young wines (MMJ) Montilla–Moriles aged wines (MMC) Villaviciosa young wines (VVJ) Villaviciosa aged wines (VVC) Total
Recognition ability (%)
MMJ
MMC
VVJ
VVC
80.0 81.8 71.4 71.4 76.2
12 2 0 0
3 9 0 0
0 0 5 4
0 0 2 10
179
Accordingly, unsupervised and supervised pattern recognition methods could distinguish Cordoban wine samples when considering their provenance and their type of elaboration (with or without ageing). Acknowledgement We express our gratitude to Villaviciosa wineries which enabled us to use samples of precisely known origin and to A.G. González for his critical review of the paper. References
and 2). Accordingly, it seems interesting to perform a discriminant analysis using the origin and the ageing of the samples as a factor for the classification. Four categories were considered, MMC and MMJ, for aged and young wines from Montilla–Moriles, and VVC and VVJ, for aged and young wines from Villaviciosa, respectively. Kruskall–Wallis test revealed differences in the content of all the mineral elements analysed, and Tukey post-hoc test indicated which variables differentiated between the four categories considered. As indicated before, Na clearly differentiates between wines from Montilla–Moriles and from Villaviciosa, and Mg differentiates between young and aged wines. The performance of LDA obtained three statistically significant discriminant functions, with sodium, zinc, potassium, strontium and cupper in the first function, magnesium, aluminum and calcium in the second and manganesum and iron in the third as the most important variables. The plot of functions one and two is represented in Fig. 3, where a good differentiation between MM and VV categories can be visualised according to function 1, and aged wines categories (MMC and VVC) can be well differentiated from MMJ young wines according to function 2. Accordingly, sodium contributes to differentiate between wines from MM and VV classes; and magnesium discriminates aged wines from young wines. The total recognition ability was 93.6% according to the four categories considerated (Table 7). Validation of these results was performed using leave-one-out cross-validation. Table 8 presents the classifications obtained for each category. It is interesting to remark that wine samples which were incorrectly classified, were classified in the category corresponding to their provenance. Thus, the model cannot differentiate —in those cases — the type of wine but the geographical origin of the wines. The total prediction ability is 76.2%. 4. Conclusions Ten elements were used to characterise southern Spanish wines from Montilla–Moriles DO and Vinos de la Tierra de Villaviciosa de Córdoba apellation. The geographical origin and the type of wines influence the final metallic contents. Thus, univariate analysis showed that wines of Montilla–Moriles can be differentiated from Villaviciosa wines mainly according to the content in sodium, manganesum, magnesium, iron and zinc. LDA showed good recognition and prediction abilities using these five mineral elements. On the other hand, young wines can be differentiated from aged wines for their content in magnesium, potassium, strontium, zinc and manganesun. Young wines were 100% classified in their category whereas aged wines were 88% when applying LDA analysis. PCA and CA showed that wine samples could be roughly grouped in accordance with their geographical origin and to their ageing. Thus, statistical differences were found in all the elements analysed when considering four sample categories according to the origin and the type of wine (young and aged wines). Prediction ability was 100% for young wines from Montilla–Moriles and Villaviciosa, and close to 90% for aged wines.
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