Geographic Origin of Wine via Trace and Ultra ... - ACS Publications

Chapter 13

Downloaded by GEORGETOWN UNIV on June 26, 2016 | http://pubs.acs.org Publication Date: December 28, 2006 | doi: 10.1021/bk-2007-0952.ch013

Geographic Origin of Wine via Trace and Ultra-Trace Elemental Analysis Using Inductively Coupled Plasma Mass Spectrometry and Chemometrics James Jaganathan, Md. Abdul Mabud, and Sumer Dugar Alcohol and Tobacco Tax and Trade Bureau (TTB), Department of Treasury, National Laboratory Center, 6000 Ammendale Road, Beltsville, MD 20705

The use of trace and ultra-trace elemental mapping of wines along with chemometrics was successfully applied to authenticate geographical origins of wines. The elemental mapping of wines was carried out using microwave digestion of the solids in a known volume of a wine in concentrated nitric acid followed by ICP-MS analysis. The concentrations of ten trace and ultra-trace elements were determined in thirty four wines from four geographical locations within the USA. The wines were produced from grapes grown in the known geographical areas. The samples comprised of white and red wines of vintages not differing by more than three years. Multivariate analyses of the elemental profiles were conducted to develop models such as principal component analysis and soft independent modeling of class analogy (SIMCA) to classify the wines. The results showed that the combination of nine elements found in wines accurately classify the products according to their geographical origin. Work is in progress to collect more authentic samples from the locations tested so far into models to make them more rugged.

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© 2007 American Chemical Society Ebeler et al.; Authentication of Food and Wine ACS Symposium Series; American Chemical Society: Washington, DC, 2006.


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The wine industry shares many goals with other food industries: reducing the costs while maintaining or increasing the quality of their product and preventing counterfeit products from entering their markets. In recent years, the Alcohol and Tobacco Tax and Trade Bureau (TTB) of the Department of the Treasury has received several complaints of counterfeit products being sold in the market. Such counterfeit products are identified through chemical analyses in comparison with an authentic reference. The Alcohol and Tobacco Tax and Trade Bureau is interested in protecting consumers from deception as well as genuine producers from fraudulent competitors. In recent years, many research papers have appeared in journals showing the use of the concentrations of several organic and inorganic species and chemometric classification models in authenticating the geographical origins of wines (1). The use of trace elements concentrations for determining geographic origins of wines has been successfully demonstrated by several scientists engaged in enological research. The concentrations of several elements, metallic and nonmetallic, were monitored in wines in the Okanagan Valley, Canada and the data were used in chemometric models for identifying their geographical origins (2). This work resulted in the successful classification of wines according to their origin. Establishing a data bank for the trace metals profiles of wines of known geographic origin will be a good foundation on which authentication work could be built. Data on organic compounds (polyphenols and anthocyanins) and trace metals have also been used to determine the origins of wines (3). A successful discriminant model was derived to differentiate Spanish rose wines. Characterization of the geographical origin of Italian red wines through elemental analyses and NMR data had been reported (4). Using chemometric methods such as principal component analysis (PCA), hierarchical clustering analysis (HCA), and discriminant analysis (DA), this research was able to differentiate wines from the North, South and Central regions of Apulia, Italy (4). Stable isotope ratios of hydrogen ( H/ H), carbon ( C/ C), and oxygen ( O/ 0), and meteorological data were used to authenticate wines from winegrowing regions of Germany (5). Classification of white wines from four German wine-growing regions using the concentrations of 13 elements and pattern recognition techniques was reported by Gomez et al. (6). The uptake of trace elements by plants is controlled by many factors such as the climate, rain, the age of the plant, the root depth, the irrigation water, and soil pH (7). However, the clarification methods used by wineries employ adsorbing materials such as silica gel, bentonite, and diatomaceous earth; these adsorbants could add trace elements to the wine. Trace elements could reflect soil compositions and would be of great help in fingerprinting wines (8). 2

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Ebeler et al.; Authentication of Food and Wine ACS Symposium Series; American Chemical Society: Washington, DC, 2006.

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In this paper we report the mapping of red and white wines from four geographical locations ( New York, Pennsylvania, Oregon, California) using 10 elements including three ultra-trace elements followed by chemometric analysis to develop classification models. These 10 elements are Ti, V, Cr, Mn, Rb, Sr, Ba, La, Ce, and U. The main goal of this study was to check the possibility of classifying wines on the basis of their trace elemental concentrations. This is a preliminary work whose viability will form the basis of a broader data bank for the authentication of geographical origins of wines.

Materials and Methods Wine Samples Wine samples of vintages ranging from 1999 to 2000 were collected from four geographical locations including New York, Pennsylvania, Oregon, and California. A l l wines were produced from grapes grown in their respective regions. Although the TTB regulations allow for the inclusion of fruit grown from outside of the stated region (upto 25%), without identifying the region, efforts were made to collect only those wines which were produced from 100% locally grown fruit. A total of 34 wines (17 red and 17 white) were used for this study as shown in Tables 1 and 2.

Sample Preparation Exactly 50 ml of a sample was placed in a Teflon tube of 'MARS 5 XP-1500 Plus' digestion vessel (CEM Corporation, Matthews, NC) and evaporated in an oven at 100°C until the volatiles were removed. The residue was digested with Table I. Description of the Wine Samples Four Red Wines

Merlot Pinot Noir Cabernet Sauvignon Zinfandel

4 different 5 different 4 different 4 different

wineries wineries wineries wineries

4 different 4 different 5 different 4 different

wineries wineries wineries wineries

Four White Wines

Savignon Blanc Chardonnay Riesling Pinot Gris


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Table II. Geographical Locations of the Wine Samples Location

Red Wines


California Oregon New York Pennsylvania

9 4 2 2

White Wines

8 4 4 1

10 ml of 30% nitric acid in the MARS 5 XP-1500 Plus digestion vessel under high pressure (1500 psig) and temperature (200°C). After cooling, the solution was transferred to a 10 ml standard flask and brought up to volume with deionized water. The solution was filtered through a 0.45μπι nylon membrane in a syringe filter and analyzed with Micromass ICP-MS. Six replicate samples were prepared for each bottle of wine.

ICP-MS Analysis Inductively Coupled Plasma Mass Spectrometry (ICP-MS) provides greater sensitivity and lower instrumental detection limits than any other rapid multi element technique (8). In this study, a Micromass Platform ICP-MS (Waters, Milford, MA) was used for determining the trace elements selected for authentication. Because of its high sensitivity, this instrument was capable of detecting these elements at low parts-per-trillion levels. The use of hexapole collision cell (He + H ) in the instrument eliminates most of the molecular interferences and enhances the sensitivity. A standard solution containing multiple elements (10 mg/L each) was obtained from Solutions Plus,Inc. (Fenton, MO) and was used for preparing calibration standards. Typically a blank and three solutions of varying concentrations (5, 10, 20 μ/L) were used. Calibration curves were generated for all elements. The precision for all of the data collected was less than 5% RSD. 2

Multivariate Data Analysis Multivariate analysis of the data on elemental concentrations was carried out on using a Pirouette, version 2.7 chemometrics software (Infometrix, Inc., Woodinville, WA). Applying the classification algorithm called 'Soft Independent Modeling of Class Analogy (SIMCA), a classification model was created for the samples analyzed.


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Results and Discussion


Elements for authentication The choice of elements plays an important role in the authentication study. The elemental composition of the wine is thought to be related to soil composition where the grapes were grown (10). Though most of the elements found in the wine reflect the elemental composition of the soil in which the grapes were grown and so this is how even regions located close to each other may be able to be distinguished (10). The trace and ultra-trace elements were selectively chosen because they can be readily determined with ICP-MS with little or no matrix interference (11). The elements chosen for this study include the trace metals Ti, V, Cr, Mn, Rb, Sr, Ba, and the ultra-trace metals, La, Ce, and U. Multivariate Data Processing The overall range of concentration of elements for all the wine samples was wide. For example, a typical wine from New York could contain up to 1130 ppb of Rb, 40 ppb of Cr and 0.50 ppb of La. In order to remove these scaling differences between variables, we decided to preprocess the data by using autoscaling. Autoscaling removes differences in units between variables by removing the mean and dividing each element by the standard deviation of that variable. Once autoscaling has been performed, each variable in the transform matrix will have a mean of zero and unit variance. We processed the data by normalizing it to the highest element concentration which was zinc. The concentration of zinc ranged from 1800 ppb to 2900 ppb. Using this normalization future instrument fluctuations could be accounted for and have a lesser effect in the ability of the chemometric model to make accurate predictions. The projections of the samples into the space of the first three principal components are shown in Figure 1. It can be seen in this figure that four distinct clusters representing samples from the four geographical regions are formed. Replicate samples cluster well together and it appears that even though the wines were from different wineries and of different vintages, they do cluster by geographical region. The total variance captured with three principal components accounts for 89.82% indicating that the systematic variance of the data set was captured with just three PCs. A SIMCA model was also created with this data set. In this study, the number of samples representing each geographic region is limited. Further study will include more authentic wine samples in the database to make the models more rugged and the data will be corrected for any elemental contributions from clarifying materials such as bentonite and silica gel.


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Figure 1. Projections of the elemental concentrations into the pace of the first three principal components (89.82% of variance captured).

Conclusion The concentrations of selected trace and ultra-trace elements in wines were used to develop classification models with multivariate analysis. Through preconcentration and acid digestion these elements can be readily determined at very low levels using an ICP-MS instrument. The study shows that the geographical origin of wines could be determined through their elemental compositions and chemometric classification models.

References 1. 2.

Edelmann, Α.; Diewok, J.; Schuster, K. C.; Lendl, B. J. Agri. Food Chem. 2001, 49, 1139-1145. Greenough, J. D.; Longerich, H. P.; Jackson, S. E. Australian Journal of Grape and Wine Research 1997, 3, 75-83.


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Perez-Magarino, S.; Ortega-Heras, M . ; Gonzalez-San Jose, M.L. Anal.

4.

Brescia, Μ. Α.; Caldarola, V.; De Giglio, Α.; Benedetti, D.; Fanizzi, F. P.; Sacco, A. Anal. Chim. Acta 2002, 458, 177-186. Christoph, N . ; Rossmann, Α.; Voerkelius, S. Mitteilungen Klosterneuburg 2003, 53, 23-40. Gomez, M . C.; Feldmann, I.; Jakubowski, N . ; Anderson, J. T. J. Agric. Food Chem. 2004, 52, 2962-2974.

Chem. Acta 2002, 458, 187-190.

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6. 7.

Brooks, R. R. Geobotany and Biogeochemistry in Mineral Exploration;

8.

Harper and Row: New York, NY, 1972. Eschnauer, H.; Jakob, L.; Meierer, H.; Neeb, R. Mikrochimica Acta 1989, 3, 291-298.

9.

Gray,

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Inductively Coupled Plasma Mass

Spectrometry; Chapman and Hall Publications: New York, NY, 1992. 10. Sun, L. X.; Danzer, K.; Thiel, G.Fres. J. Anal. Chem. 1997, 359, 143-149. 11. Jarvis, Κ. E.; Gray, A. L.; Honk, R. S. Handbook of Inductively Coupled Plasma Mass Spectrometry; Chapman and Hall Publications: New York, NY, 1992.


Geographic Origin of Wine via Trace and Ultra ... - ACS Publications

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