Authentication of Food and Wine - American Chemical Society

Chapter 12

Downloaded by AUBURN UNIV on September 12, 2017 | http://pubs.acs.org Publication Date: December 28, 2006 | doi: 10.1021/bk-2007-0952.ch012

A Simplified Approach to Wine Varietal Authentication Using Complementary Methods: Headspace Mass Spectrometry and FTIR Spectroscopy Neil Pennington, Fan Ni, 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

Two methods for wine varietal authentication are evaluated using two complementary analytical approaches. A mass spectrometer is used to detect the headspace volatiles and fourier transform infrared (FTIR) spectroscopy is used to measure the phenolic compounds of four red and four white wine varietals from 18 different wineries across the United States. The spectra generated by each technique is analyzed by chemometric techniques to differentiate between the varietal wines. K-Nearest Neighbor (KNN) and Soft Independent Modeling of Class Analogy (SIMCA) models are developed for varietal authentication. Partial Least Squares (PLS) models are developed to model the binary mixtures between varietal wines and to determine the contributions of the varietals in wine mixtures. Chemometric models based on the MS volatiles spectra and the FTIR phenolics spectra offer a simple and fast approach to analyze and classify the varietal nature of wines.

180

© 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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Introduction The character and quality of a wine is dependant on the grape varietal used in the manufacturing procedure. Understanding the differences between varietals is becoming extremely important in wine consuming countries where there is an opportunity for fraud and premium prices are paid for excellent quality products. Consumers place a great deal of trust on labels as to the composition and quality of the product. A fraudulent producer could gain advantage in a market place by providing incomplete or misleading information about their product. Grape varietal adulteration is defined as adding must made from grapes originating from varieties other than the labeled varietal in quantities exceeding the limits specified by law. In the United States of America, at least 75% of the wine must be derived from grapes of the variety specified on the label (1). Currently there are no methods available to effectively distinguish between different varietal wines. To differentiate between wine varietals, various approaches have been studied: analysis of the volatile content (2,3), amino acid profiles (4,5), protein fractions (6,7), physical parameters (8), and phenolic compounds (9-16). Although these methods provide valuable information, most of them are time consuming and do not provide conclusive information for varietal authentication. Recently, efforts have been made in our laboratory using a different approach to explore wine varietal authentication. As an alternative to measuring for specific components, a set of spectra measuring multiple components, is generated and analyzed by chemometric algorithms to differentiate between varietals. A l l compounds that contribute to the varietal uniqueness of the wine within the spectra are included in the analysis of the wine varietals. Currently, two techniques are being used in our laboratory to measure complementary aspects of wine varietals. Thefirstmethod uses headspace mass spectrometry and chemometrics to profile the volatile components of wine varietals. It is well known that different varietal wines contain different composition of aroma or volatile components. The second method uses midrange FTIR and chemometrics to profile the phenolic compounds of varietal wines. The type and amount of the phenolic compounds are very important to the overall chemical composition of a wine. They are the major contributor to the taste and color of wines and are responsible for the stability of wines as they age. Detailed descriptions of the phenolic compounds are discussed in a previous review (10). The techniques presented do not require extensive sample preparation procedures to measure the spectra. We report here thefindingsof the two approaches for wine varietal authenticity.

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

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Materials and Methods Wine Samples.


Wines of eight pure varietals were collected from various wineries across the USA from the 1998, 1999 and 2000 harvest year. Each wine was certified at the winery by TTB investigators as being 100% pure varietal. For each grape varietal, the wines were collectedfromdifferent wineries (Table 1).

Table 1: Description of the 100% Varietal Wine Samples Four Red Wines

Merlot (ME) PinotNoir (PN) Cabernet Sauvignon (CS) Zinfandel (ZN)

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

Sauvignon Blanc (SB) Chardonnay (CH) Riesling (RE) Pinot Gris (PG)

Measurement of the Volatile Content Using Headspace Mass Spectrometry. Sample Preparation:

For each wine sample, the following sample preparation steps were followed: 1.

2. 3. 4.

The wine bottles were soaked in ice water for at least 10 minutes before opening the bottle to minimize the volatile content escaping from the wine. Then 5 mL of wine sample was transferred into a 10 mL vial, and the vial was immediately sealed by crimping the vial. Ten (10) replicate samples were prepared for each bottle of wine. For blending experiments, mixed samples from two different bottles of wine were prepared at varied mixing ratio. Test samples were prepared by another chemist from the original set of wines. They included pure varietal wines and binary mixtures of the varietal wines.


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5.

The samples were analyzed on the same day by a Gerstel headspace mass spectrometer ChemSensor (Gerstel Inc, Baltimore, MD).


Headspace Analysis:

Measurement of each sample was performed under the following conditions: incubation temperature: 85°C, sample incubation time: 20 minutes, syringe temperature: 90°C, injection port temperature: 180°C and MSD interface temperature: 200°C, injection volume: 1 mL, split mode: splitless for 0.5 minutes. The temperature was increased steadily from the incubation chamber to the MSD interface to prevent the sample volatile constituents from being condensed at each step. The sample was introduced into the ionization source without prior separation with a scan range of 50-150 amu. All the mass spectra obtained for a wine sample were accumulated to generate a composite mass spectrum. This composite mass spectrum represents a fingerprint of the wine. Figure 1 shows a typical composite mass spectrum of a wine sample. To avoid the interference from ethanol, which generates ions at 45 and 46 amu, the scan range was started at 50 amu. Since there were no major ions produced from wine samples beyond 150 amu, the scan range was set up from 50 to 150 amu. Pirouette (version 3.11, infometrix, Bothell, Washington) was used to process the mass spectrum data. The data was preprocessed by vector normalization. Two classification models, K-Nearest Neighbor (KNN) and Soft Independent Modeling of Class Analogy (SIMCA) were developed using the composite mass spectra of the wines.

Measurement of the Wine Phenolics Using FTIR. Phenolic Extract Preparation:

To measure the phenolic profiles of the wines it was important to pre-treat the samples. The absorptions from ethanol, carbohydrates, water, and organic acids will mask the absorptions from phenolic compounds if left untreated (Figure 2). Solid phase extraction (SPE) using C-18 sorbent has been shown to be an effective phase to separate the phenolic components from other interferences in wine (17). All wine samples were filtered before SPE with a disposable syringe filter (0.45μηι pore size). Bond Elute solid phase cartridges (Varian, Walnut Creek, California), containing 1000 mg of C-18 sorbent in a 3 mL cartridge, were



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preconditioned with 10 mL of methanol and 15 mL of distilled water (14). Three mL of red wine or 6 mL of white wine were loaded onto the column. A higher volume of white wine was used to concentrate the phenolics. The cartridges were then washed with 20 mL of deionized water. The extracts were eluted with six portions of 0.5 mL of acidic methanol (0.01% HCL). The SPE procedure was automated with a Rapid Trace SPE Workstation (Caliper Life Sciences, Massachusetts, USA).

Figure 2. Spectrum of a red wine sample and phenolic extract

Measurement and Analysis of Phenolic Profiles:

Measurements were collected using a Bruker Tensor 37 FTIR (Billerica, Massachusetts) equipped with a single bounce attenuated total reflection (ATR) from SensIR (Danbury, Connecticut). Data was collected over the range from 4000 - 700 cm" , at a resolution of 4 cm" (300 scans, apodization function: Blackman-Harris-3-term). A Spectrum of the clean, dry ATR crystal against air was used for the background. One of red wine phenolic extract was transferred onto the ATR crystal and allowed to dry. For the white wines, a total of 2 μΐ, was used, 1 μΐ, was added and allowed to dry before an additional 1 μ\^ was added. Spectra were recorded after complete drying of the samples. Four replicate spectra were recorded for each of the pure varietal winesfromthe same bottle, and two replicate spectra were recorded for the selected mixtures of the wines and test samples. 1

1



186

Analysis of the FTIR spectra was carried out using chemometric software Pirouette. Samples were preprocessed by taking the first derivative (SavitskyGolay algorithm, 9 points) and vector normalization. Principal Component Analysis (PCA) scores plots were use to visualize the separation of the wine varietals. Κ Nearest Neighbors (KNN) and Soft Independent Modeling of Class Analogy (SIMCA) models were developed to model the differences between the grape varietals. Separate models were created for red and white wines. Because of the small sample size, leave-one-out validation procedures were used to validate the models. The models were used to classify test samples consisting of 100% pure varietal samples and binary mixtures of pure varietals. Partial Least Squares (PLS) models were created to demonstrate the regression between different concentration of varietals within blended samples.

Results and Discussion

Modeling of Wine Varietals by the Volatile Content.

The mass spectrum obtained is a composite of all fragmented ions derived from various components in the wine vapor without prior separation. Two Chemometrics classification models, K-Nearest Neighbor (KNN) and Soft Independent Modeling of Class Analogy (SIMCA), are developed using the composite mass spectra of the wines. In Figure 3, a plot of three-dimensional SIMCA class distances for white wines is shown. Each wine sample is replicated 10 times. The four white varietal wines form separate clusters with a certain distance separate from each other. The interclass distances between individual clusters is greater than three, which indicate an adequate separation between the wines (18). In Figure 4, the three-dimensional KNN class fit plot of four white wines is observed, which forms four separated clusters. But, some overlapping between Riesling and Sauvignon Blanc at the bottom region of the plot, and between Pinot Gris and Chardonnay at the top is observed. Both KNN and SIMCA models are established as preliminary models. Test samples, prepared by a second chemist from the original set of wines, are classified by both models. The results are listed in the Table 2. Both models classify all 100% pure varietal test samples correctly, however, further testing of the model is necessary with winesfromoutside the validation set in order to fully test this method. Vintage and manufacturer are also considered but did not have a significant effect on the model.


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FFigure 4. Three-dimensional KNN classfitfor Riesling (RE), Pinot Gris (PG), Chardonnay (CH) and Sauvignon Blanc (SB) using volatiles data.


189


Table 2: Classification of white wine test samples from volatiles analysis Test Sample

KNN Prediction

SIMCA Prediction

Actual

1 2 3 4 5

PG CH CH RE CH

PG CH CH RE CH

100 PG 100 CH 100 CH 100 RE 100 CH

CS2@9

ME Figure 5. Three-dimensional SIMCA Class Distance for Cabernet Sauvignon (CS), Zinfandel (ZN), Merlot (ME) and Pinot Noir (PN)from volatiles analysis.


190


In Figure 5, a plot of the three-dimensional SIMCA class distances for red wines is shown. Four red varietal wines form separate clusters with certain distance from each other. The interclass distances between individual clusters is greater than three, which indicate a good separation between the wines. In Figure 6 is the three-dimensional KNN class fit plot of four red wines. Zinfandel slightly overlaps with other three wine varietals. Merlot, Pinot Noir, and Cabernet Sauvignon form separate clusters.

Figure 6. Three dimensional KNN classfitfor red win s using data from volatiles analysis. Cabernet Sauvignon (CS), Zinfandel (ZN), Merlot (ME), Pinot Noir (PN).

The KNN and SIMCA models of the red wines are used as preliminary models, their suitability is tested to classify some red wine samples without knowing the identities of the samples at the time of testing. The classification results are listed in Table 3. The classification rate for the KNN and SIMCA models is 92% and 83% respectively.

Modeling of Wine Varietals by the Phenolic Components. In Figure 2, the absorptions of interest in the mid-infrared region are detected from 1800 cm" to 800 cm" . The phenolic profile in this region of the 1

1


191 Table 3: Classification of red wine test samples from volatiles analysis


Test Sample

1 2 3 4 5 6 7 8 9 10 11 12

KNN Prediction

SIMCA Prediction

ZN PN CS CS ME ZN CS

ZN PN CS CS ME ZN CS

ZN

ZN

PN CS ME PN

PN CS ME ME

Actual

100 ZN 100 PN 100 CS 100 CS 100 ME 100 ZN 100 CS 100 ME 100 PN 100 CS 100 ME 100 PN

NOTE: Samples that were incorrectly predicted are in bold.

spectrum is heavily influenced by the aromatic nature of phenolic compounds. It is impossible to label specific absorptions within the spectrum because of the complex and diverse nature of the phenolic compounds. However, between 1800-1500 cm" there are strong absorptions from mono and di-substituted carbon compounds. In the middle region of the spectrum, from 1500-1250 cm" , are hydrocarbon bending vibrations that influence the spectra. Other important vibrations include O-H phenol vibrations from 1200-1000 cm" . The composition of phenolic compounds differs among each varietal leading to a different absorbance pattern for each varietal. Discrimination of the red wine varietals is achieved by analysis of the phenolic profiles for each wine. Principal component analysis (PCA) scores plots are analyzed to visualize separation between the varietals. Factors 1 and 2 describe the difference between Pinot Noir and the other three varietals. Discrimination of all four varietals is illustrated in factors 3, 4,and 5 (Figure 7). In this plot, the wines cluster in regions according to the wine varietal. The separation is optimized by using the FTIR spectrum range 1800-1000 cm" . KNN and SIMCA models are developed to model the separation of the varietals. The KNN model is developed using the three nearest neighbors. The models are validated according to the leave-one-out approach. In this approach a model is created excluding one of the samples. The model created is used to predict the sample left out. This procedure is repeated for each sample in the data set. The results are shown in Table 4. Merlot, Pinot Noir, and Zinfandel are well separated according to the results. Two of the Cabernet Sauvignon 1

1

1

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192

es

Figure 7. Three dimensional PCA scores plot for Cabernet Sauvignon (CS), Zinfandel (ZN), Merlot (ME), and Pinot Noir(PN) using phenolics data. Variance are 8%, 6%, and 3% for factors 3, 4, and 5 respectively.

Table 4. Leave one out validation results for the red wine KNN and SIMCA models using phenolics data Varietal Cabernet Sauvignon Merlot Pinot Noir Zinfandel

# ofsamples 4 4 5 4

KNN (3) 2 4 5 3

SIMCA 2 4 5 4

NOTE: Number of samples predicted correctly.



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samples are not classified correctly according to the validation method. The effects of the vintage year and the manufacturer are investigated as reasons for the misclassifications, but no conclusions could be drawn. More samples are needed to better model the variations within Cabernet Sauvignon. To assess the models, test samples are classified with the red wine models. The test samples, are prepared by a second chemist from the original wines, and include pure varietal wines and selected mixtures of the pure varietals. The results from the test samples are shown in Table 5. All of the pure varietal samples are correctly classified, as well as the majority of the mixed samples. These results provide preliminary evidence that the models will be able to predict the major grape varietal in wines that are not 100% pure varietal, however more work with a larger number of samples is needed.

Table 5. Classification of red wine test samples using phenolics data. Test Sample

KNN (3) Prediction

SIMCA Prediction

Actual

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

ZN ZN ME ME ME CS PN ZN ZN PN ME CS PN ME ZN ME CS PN PN ZN ZN CS

ZN ZN ME ME ME CS PN ZN ZN PN ME CS PN ME ZN ME CS PN PN ZN ZN ZN

100 ZN 100 ZN 100 ME 100 ME 100 ME 100 CS 100 PN 100 ZN 80ZN/20 CS 80 PN / 20 CS 75 M E / 2 5 ZN 75 CS / 25 ZN 75PN/25ZN 75 M E / 2 5 ZN 75ZN/25PN 75 M E / 2 5 PN 75 CS / 25 ME 75 PN/25 ME 75PN/25 CS 75ZN/25PN 75ZN/25 CS 75 CS / 25 ZN

NOTE: Samples that were incorrectly predicted are in bold.



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Discrimination of the white wine varietals is achieved through analysis of the phenolic spectra. As a comparison to red wines, white wines have a lower amount of total phenolics because the fermentation of the wines does not involve the seeds and skins. A lower phenolic content decreases the absorbance in the mid-infrared region making it more difficult to record the specific profile of each varietal. To increase the sensitivity, 2 μι of extracted phenolic is used for white wines as compared to 1 μί for red wines. KNN and SIMCA models are prepared in accordance with the red wine models and are validated with the leave-one-out approach (Table 6). The Riesling and Sauvignon Blanc varietals are well separated according to the validation results. Analysis of the validation of Chardonnay and Pinot Grigio shows a small separation, which results in several misclassifications for the two varietals. Table 6. Leave one out validation results for the white wine KNN and SIMCA models using phenolics data. Varietal

Chardonnay Pinot Grigio Riesling Sauvignon Blanc

# ofsamples

4 4 5 4

KNN (3) 2 2

4 4

SIMCA

3 2 4 3

NOTE: Number of samples predicted correctly. Test samples are classified with the white wine models. The test samples include pure varietal samples and selected mixtures of the pure varietals. The test mixtures consist of 75% of one varietal and 25% of a second varietal. The results are shown in Table 7. Only two of the test samples are 100% varietals and both of the samples are predicted correctly. The mixed samples had different results for the two models. The SIMCA model classified more samples correctly than the KNN model. One possible reason for some of the misclassifications is due to an increase in sample variation within the white samples. The variation is a result of the low phenolic content and the extra steps that are needed to record a spectrum. However, the majority of the blind sample are classified correctly providing support that the model will have the ability to predict white wine varietals. Prediction of Varietal Contributions in Blended Wines.

Estimating the percentage of a varietal in a mixed wine sample is possible by measuring either the headspace or phenolic profile and using multivariate calculations. To demonstrate this, the phenolic profile of a set of three wines (Zinfandel, Pinot Noir, and Merlot) is measured and processed by partial least squares (PLS) algorithms. The projections of these pure wines, along with their binary mixtures (75/25, 50/50, and 25/75 for each pair), into the 2 factor space is shown in Figure 8.


195

Table 7. Classification of white wine test samples using phenolics data.


Test Sample

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

KNN (3) Prediction

SIMCA Prediction

Actual

PG SB

PG SB PG CH PG CH RE RE RE PG SB SB SB CH

100 PG 100 SB 75 PG / 25 RE 75CH/25RE 75 PG/25 CH 75CH/25PG 75 RE/25 PG 75 RE / 25 CH 75 RE / 25 SB 75 PG / 25 RE 75 SB / 25 RE 75 SB / 25 PG 75 SB / 25 PG 75 CH / 25 PG 75 SB/25 CH 75 SB / 25 RE 75CH/25RE 75CH/25RE 75 R E / 2 5 CH 75 RE / 25 SB 75 SB/25 CH 75 SB / 25 RE 75 C H / 2 5 SB 75CH/25PG 75 CH/25 PG

CH

CH PG CH RE RE CH

PG PG

SB SB CH SB RE

CH CH RE RE SB CH

CH CH PG

PG RE

CH CH RE RE SB SB CH CH CH

NOTE: Samples that were incorrectly classified are in bold.



196

Factor! 53% variance

Figure 8. PCA scores plot of three wines of different varietals and binary mixtures of the varietal wines using phenolics data. Merlot (ME), Zinfandel (ZN), and Pinot Noir (PN).

Inspection of PCA scores plot (Figure 8) reveals a correlation between the pure varietal samples and the mixed samples. The three 100% varietals create a triangle, and the resulting binary mixtures form the sides of the triangle. Factor 1 is interpreted as the difference between Pinot Noir and the other two pure wines. Factor 2 is interpreted as the difference between Zinfandel and Merlot. The closer a mixed sample is to a pure varietal in factor space, the higher the concentration of the varietal in the mixed sample. For example, there are three ratios of Merlot and Zinfandel. The primary component in mixture 9 is Zinfandel, whereas the primary component in mixture 7 is Merlot. Mixture 8 is a 50/50 mixture of the two varietals. The same relationship holds for the other two sets of pure varietals. The relationship between the varietal wines is modeled by PLS algorithms. Within the PLS model, a 5-point percentage plot, from 0 to 100 percent, is created for each varietal (Figure 9). The models are used to predict the varietal composition of unknown samples. Six samples, of unknown varietal


197

concentration, are predicted using the PLS models (Table 8). On average, the models from the volatile components and the phenolics are able to predict the major component within 5% of the actual content.


Conclusion Discrimination of pure varietal wines is demonstrated with two different complementary analytical methods. Headspace mass-spectrometry profiled the volatile components and fourier transform infrared spectroscopy measured the phenolic compounds. Classification models (KNN and SIMCA) are created from the headspace volatiles that fully discriminate between the pure varietal wines. The models are then used to classify pure varietals of red and white wine. All the white wines are classified correctly and the majority of the red wines are classified correctly. The classification models for the phenolic spectra completely discriminate between the pure varietal wines. A higher degree of separation is observed in the red wine models which is attributed to the fact that red wines have more phenolics than white wines. Successful classification of the mixed samples provides confidence that the models will be able to identify wines that are not pure varietals. Future work is in progress to expand the number of pure varietal wines in the modeling set to account variations observed in the classification models. PLS models are demonstrated as a powerful tool to classify the percentage of a varietal in a wine sample. Models are constructed from two pure varietal wines and binary mixtures of the two wines. The models correctly classify

Table 8. P L S classification of test samples using phenolics data Test Sample

1 2 3 4 5 6

Predicted Value

101 M E 75PN/25ZN 40PN/60ZN 40ZN/60ME 80 ME / 25 ZN 71 ZN / 29 PN

Actual Value

100 M E 75PN/25ZN 50 PN / 50 ZN 50 ZN / 50 ME 75 ME / 25 ZN 75ZN/25PN

NOTE: data listed is the varietal percentage



198

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40

80

Measured ME

Figure 9. PLS model for the prediction of the % ME in an unknown sample using phenolics data.

samples composed of mixtures of the wines from the model. PLS models created from the spectrum of the phenolics compounds and volatile compounds identified the varietal concentration of blind samples with an average deviation of 5%

Acknowledgements A special thanks to the Trade Investigation Division of the Alcohol and Tobacco Tax and Trade Bureau (TTB) for collecting the authentic wine samples and to the U.S. wineries for providing the wines.

References 1. 2. 3.

U.S. Code of Federal Regulations (CFR) 27:4.23.b Petka, J.; Mocak, J.; Farkas, O. J. Sci. Food Agric. 2001, 81, 1533-1539. Ortega, C.; Lopez, R.; Cacho, J.; Ferreira, V. J. Chrom. A, 2001, 923, 205214.


199 4. 5. 6. 7. 8.

Herbert, P.; Barros, P.; Alves A. Am. J. Enol. Vitic. 2000, 51, 262-268. Vasconcelos, A.M.P.; Chaves das Neves, H.J. J. Agric. Food Chem. 1989, 37, 931-937 Pueyo, E.; Dizy, M.; Polo, M.C. Am. J. Enol. Vitic. 1993, 44,255-260 Garcia-Beneytez, E.; Moreno-Arribas, M.V.; Borrego, J.; Polo, M.C.; Ibanez, J. J. Agric. Food Chem. 2002, 50, 6090-6096. Etievant, P.; Schlich, P.; Cantagrel, R.; Bernard, Α.; Bouvier, J.C. J. Agric. Food Chem. 1989, 46, 421-438.


9. 10. 11. 12. 13. 14.

Edelmann, Α.; Diewok, J.; Schuster, K.C.; Lendl, B. J. Agric. Food Chem. 2001, 49, 1139-1145. Waterhouse, A.L. Ann. N.Y. Acad. Sci. 2002, 957, 21-36. Calvo, D.; Saenz-Lopez, R.; Fernandez-Zurbano, P.; Tena, M.T. Anal. Chim. Acta, 2004, 524, 207-213. Hong, V.; Wrolstad, R.E. J. Agric. Food Chem. 1990, 38,708-715. Lamuela-Raventos, R.M.; Waterhouse, A.L. Am. J. Enol. Vitic. 1994, 45, 1-5. Baldi, Α.; Romani, Α.; Mulinacci, N.; Vincieri, F.F.; Casetta, Β. J. Agric. Food Chem. 1995, 43, 2104-2109.

15. Mazzuca, P.; Ferranti, P.; Picariello, G.; Chianese, L.; Addeo, F. J. Mass Spec. 2005, 40, 83-90. 16. Patz, C D . ; Blieke, Α.; Ristow, R.; Dietrich, H. Anal. Chim. Acta, 2004, 513, 81-89. 17. Cocciardi, R.A.; Ismail, A.A.; Sedman, J. J. Agric. Food Chem. 2005, 53, 2803-2809. 18. Schafhalter, A.K.; Fuchs, H.; Pfannhauser, W. J. Sci. Food Agric. 1998, 78, 435-440.


Authentication of Food and Wine - American Chemical Society

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