Application of quality control methods for assessing wine authenticity: Use of multivariate analysis (chemometrics)

Trends in Food Science & Technology 10 (1999) 321±336

Review

Application of quality control methods for assessing wine authenticity: Use of multivariate analysis (chemometrics) I.S. Arvanitoyannis*,{, M.N. Katsota*, E.P. Psarra*, E.H. Sou¯eros*, S. Kallithrakay *

Department of Food Science & Technology, Faculty of Agriculture, Box 265, Aristotle University of Thessaloniki, Greece (tel: +30-31-998788; fax: +30-31-998789; e-mail: [email protected]) y Mediterranean Agronomic Institute of CHania (MAICH), Alsyllion Agrokepion, P.O. Box 85, GR-73 100 Chania, Crete, Greece

Determination of food authenticity is one of the most crucial issues in food quality control and safety. The introduction of new sophisticated techniques, in conjunction with greater consumer demands and expectation for safer products, gives a tremendous impetus to food quality assurance. Wine adulteration, mainly in terms of varieties and regions of origin (geographical), has been very widespread. {

Corresponding author.

Therefore, apart from novel experimental techniques, the need emerged for a more comprehensive statistical data analysis. Multivariate analysis comprising principal component analysis (PCA), discriminant analysis (DA), canonical analysis (CA), cluster analysis (CLA), has, in most cases, been e€ectively employed in wine di€erentiation and classi®cation according to geographical origin. # 2000 Published by Elsevier Science Ltd. All rights reserved.

Authenticity of foods and, in particular, of wine has been extensively investigated because wine is an easily adulterated product due to its strong chemical basis (high alcohol content, low pH) and its availability throughout the world [11]. Meticulous and continuous controls are required to maintain the quality of wine. The authenticity of wine is guaranteed by strict guidelines laid down by the responsible national authorities (e.g. Institut National des Appellations d' Origine in France) which include ocial sensory evaluation, chemical analyses and examination of the register kept by the wine producer. Wine mobility in bulk containers within the European Community is also carefully controlled requiring transport documents which certify authenticity as de®ned by EC directive 986/89 [2]. There is currently a great range of combined techniques employing group classi®cation to identify wine's authenticity. In general, analysis of volatile compounds is used to characterize di€erent varieties such as the contribution of ethyl esters of fatty acids and acetates of higher alcohols from neutral grape varieties [3]. Aroma components of wine have been isolated and identi®ed and various laboratory methods, such as high-pressure liquid chromatography (HPLC), gas chromatography (GS), atomic absorption spectroscopy (AAS) and, more recently, capillary gas chromatography (CGC) and mass spectrometry (MS). Garcia-Jares et al. [4] managed to di€erentiate several white wines of Galicean origin comparing 19 highly volatile compounds and eight aroma compounds used to characterize Spanish white wines from the PenedeÂs area in Catalonia [5]. Minerals are usually employed for the identi®cation of geographical origin [1]. Stable isotope analysis of fermented grape juices has employed 2H-NMR spectroscopy and isotope ratio mass spectroscopy in combination with elemental determination performed by AAS using ¯ame

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and thermal ionization [6]. The most commonly employed isotopes for wine identi®cation are carbon (13C), hydrogen (2H) and oxygen (18O). Deuterium also occurs in ethanol produced by fermentation and is related to the deuterium content of the water and the sugar molecules. This isotope has been successfully used for detecting the presence of exogenous sugars in wines [7]. Moreover, the SNIF-NMR method was ocially adopted in 1987 by the International Oce of Vine and Wine by the Commission of the European Communities (CEC) as a means of detecting the chaptalization (sugar addition before fermentation) of wine with beet sugar. Chaptalization is only allowed for certain wines in some areas of France, to increase the natural alcoholic strength in areas where the natural sugar content of the must is too low [8]. Numerous articles have appeared involving trace elements [9,10]. It is important to evaluate the extent mineral content of the must re¯ects that of soil. Rubidium and lithium appear to play a key role in geographical classi®cation [11]. Moreover, ultra-trace elements (the lanthanides) were reported to substantially assist wine di€erentiation. Inductively coupled plasma/mass spectrometry (ICP/MS) is frequently considered to be one of the best techniques for such determinations [12]. Apart from trace elements, amino acids constitute another useful group of substances for wine di€erentiation and classi®cation. This was used to separate authentic Champagnes from sparkling wines where the second fermentation to produce the over pressure of CO2 is performed in the bottle leading to an increase in amino acids [13]. Proline, hydroxyproline and ethanolamine showed clear di€erences between 34 French red wines [14]. The determination of amino acids was also conducted with the TNBS method [15]. Moreover, it is well-known that the amino acid content of grapes is dependent upon fertilization, climatic conditions and duration of skin maceration in the must. Despite these problems some researchers have successfully employed amino acid composition for wine classi®cation [16±18]. Sou¯eros et al. [19] managed to classify wines of various regions (Bordeaux, Bourgogne, Alsace and Champagne) by employing HPLC for determination of amino acids, biogenic amines and organic acids and GC for volatile determination. Furthermore, Sou¯eros and Bertrand [16] adapted the method for amino acid determination using HPLC with ¯uorescence detector to experimental requirements showing the merits of this modi®ed method. In a very interesting study, methionine, proline, asparagine, arginine and glutamic acid were used to di€erentiate 34 samples of grape, apple and pineapple juices [20]. Phenolics are another promising class of compounds widely used to categorize wines on the basis of changes in absorbance (A420, A280). Caftaric and coutaric acids as well as procyanidins were compounds that showed considerable di€erences between two wine varieties from Spain [21]. Furthermore, the ¯avonoids

myricetin and quercetin were determined by gradient reversed-phase HPLC and successfully used to classify red wines of various geographical origins [22]. For wines from the MeÂdoc area, discrimination was achieved by two-dimensional analysis of myricetin and epicatechin levels [23]. In this way, an e€ective di€erentiation was established among seven commercial brands of dry sherry wines on the basis of only ®ve phenolic compounds [24]. Di€erentiation of vinegars produced from di€erent wines from the south of Spain was also made possible by employing phenolic compounds [25]. Sensory evaluation was considered, until recently, the only valid way to classify wines according to vintage and origin. Frank and Kowalski [26] showed that sensory data do not provide sucient information to separate wines from various areas of France and USA. Only 20% of experienced tasters provided correct answers [27]. Veri®cation of wine authenticity may lay in the joint application of two or more techniques such as isotopes and minerals. Amino acids and volatile compounds have been employed as a legal basis for detecting adulterated wines, since the phenolic content of wine is a good criterion for safely detecting both variety and region. In future, DNA sequencing and PCR techniques will most probably be used as an ultimate measure of authenticity [28]. An interpretation of wine di€erences, related to varietal origin, was based on the results of statistical analysis [29]. A variety of methods of multivariate data analysis can be very useful to explore the structure of such data [30]. Several multivariate methods have been applied for classifying wines, vinegars or juices. As far as the juices are concerned, the following statistical methods have been used in order to distinguish grape, apple and pineapple juices. Principal component analysis (PCA) is used to establish the relationships among variables [31]. Cluster linear analysis (CLA) is employed to discover natural groupings of samples [32]. Stepwise discriminant analysis (SDA) is applied to select the most important variables di€erentiating three types of juices [33] and multiple linear regression analysis (MLRA) to estimate the grape content in mixtures [34]. PCA is a frequently used statistical analysis and has been successfully applied to the analytical results both for individual compounds and to component combination. For example, PCA with discriminant analysis (DA) was applied to anthocyanins, ¯avonoids and color parameters determined in Spanish red wines aged in wood showing considerable di€erences among various wines depending on the appellation of origin for the region [35]. A relatively new method that has been recently applied is generalized canonical analysis (GCA) [36]. Employment of GCA in classi®cation of Spanish wines according to region of origin using seven elements was carried out showing that satisfactory di€erentiation of country of origin is possible [12].

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Another nonlinear multivariate method is CLA which analyses data on a nominal level providing a graphical representation of a table that stands for a cross-tabulation of two variables consisting of separate categories [37]. This can prove to be an extremely useful method, especially with large cross-tables, because it is often much easier to visualize a graphical representation of the data than a tabulation [38]. CLA showed that polyalcohol determination is a promising tool to discriminate vinegar raw materials for white products [39]. The canonical discriminant analysis (CDA) is a method applied to identify similarities between wines from the same region of origin and main di€erences between wines from di€erent regions. Applying the above method in conjunction with PCA, 22 French red wines were satisfactorily classi®ed according to their geographical origin [40]. Among the various multivariate techniques, the simplest seems to be the LDA method. The distinction between two categories is a linear function; for example, a straight line in the case of two independent variables [41]. LDA in conjunction with PCA and SDA were applied for di€erentiation of wine vinegars, based on phenolic compounds [25]. Multivariate analysis methods are becoming increasingly popular and important for determining wine authenticity. An adequately accurate separation can be accomplished employing DA. However, an e€ective classi®cation of wines into groups requires knowledge of many more factors, such as volatile acidity, alcohol content, climate conditions and microbiological and enological parameters during fermentation which a€ect wine stability [42] and will be analyzed in the following sections.

Distinction of wines according to geography, varieties and aging characteristics Minerals

A comprehensive approach to wine authenticity can be provided by isotopic determination. The technique of NMR can supply important information on the provenance of a given wine [43]. Deuterium content of water and ratios of the methyl group of ethanol are employed by the SNIF-NMR. Furthermore, it is dicult to determine the vintage of a wine and strict quality control procedures need to be applied at analytical measurements. The determination of 14C content of a wine can be successful in vintage year determination. However, a problem exists because of steady increase in atmospheric 14C in the 1950s and 1960s as a result of nuclear testing. Since levels have been decreasing for several years, it is conceivable that young and old vintages could contain similar 14C levels. The 2H/1H value of ethanol is clearly correlated with the environmental conditions of the vintage year. Reliable environmental data are required on the site where grapes were grown in order to use 2H/1H values e€ectively [44].

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One of the oldest methods for determining the age of an alcohol is based on the 18O content of water and 13C content of wine distillate [45]. Measurement of 13C levels has an advantage over 2H in that 13C is considerably more abundant than 2H, and is also slightly more sensitive in NMR spectroscopy experiments. However, a disadvantage of using the 14C nucleus is the long relaxation times encountered, which can considerably extend analysis times. Quantitative deuterium nuclear resonance spectroscopy has been employed and absolute natural abundance deuterium/hydrogen isotope ratios of ethanol were used to characterize the origin of Valencian varietal grape musts. Deuterium molecular distributions patterns of ethanol have di€ered according to geographical and varietal origin of the samples [46]. The isotopes are very dependent on climate and variety and despite their usefulness they can not be directly correlated with the year of production of wine [9]. In order to provide complementary data, stable isotopes were combined with trace elements [47]. Elements can be considered as good indicators of wine origin since they are not metabolized or modi®ed during the vini®cation process. Also, one has to bear in mind the fact that grape pressing and skin maceration time can substantially a€ect the ®nal concentrations of elements in wine [48]. The most frequently quanti®ed elements are: K, Na, Fe, Zn, Rb, Ca, Mg, Mn, Cu, Cr, Co, Sb, Cs, Br, Al, Ba, As, Li, Ag [10±12, 41, 49]. Some authors have succeeded in identifying wine origin only by analysis of element content. The Spanish wines have been classi®ed according to geographical origin and to wine type using 48 elements [12]. Classi®cation of Spanish wines from Galicia was e€ected by using only two mineral elements, calcium and barium [50]. A correct classi®cation of three French wines from di€erent regions was obtained based on Li, K, Ca, Cu, Ni content and employment of linear discriminant analysis as well as other classi®cation techniques [51]. The potential of multi-element analysis for determining the region of wine origin was shown by McCurdy et al. [52] and, more recently, Baxter et al. [12], who managed to identify the origin of Spanish wines coming from three di€erent regions. The usefulness of lanthanides or rare earth elements has also been clearly demonstrated since La, Ce, Pr, Nd, Eu, Gd, Tb, Ho, Er, Tm, Yb, Lu are some of the lanthanides successfully employed for distinction between regions [52]. Another promising method for detecting wine authenticity is lead content determination. Wines with a content of under 100mg/l are considered not to be polluted by the material during the wine making process or storage. Determination of organo-lead (ethyl lead and methyl lead) was achieved using a special hyphenated technique (gas chromatographyÐmicrowave-induced helium plasmaÐatomic emission spectrometry) [53]. This comparative study of organo lead levels in a homonym

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series of wines covering over 40 years in the same area showed level ¯uctuations. Triethyl lead was present in the 1960s but its levels dropped after 1980. The use of this time shift versus the relative proportion of methyl and ethyl lead allows one to plot concentration curves permitting an evaluation of the age of the wine from the 1960s to the present. Absence of organo-lead in wine implies that wine originates from countries having banned those compounds many years ago or is a very recent European wine. Detection of high levels of organo-lead implies proximity to a heavily used road or a city. According to the vintage, the relative proportion of trimethyl and triethyl indicates the year of production [1]. Lead has also four isotopes, three of which come from decay of uranium and thorium and they exhibit greater variation than lighter elements. Graphical representation of the isotopic ratios has shown a clear distinction of wines. Another role of the isotopic lead in authenticity is that the isotopic determination of 206Pb/204Pb shows a de®nite variation according to the year of production [54].

Phenolic compounds

Phenolic compounds have been successfully employed for assessing wine authenticity. Phenolic content characterizes the varieties and provides information for the geographical origin. It is well known that phenolics are ingredients of musts and wines responsible for browning. If must is kept in contact with oxygen, the oxidation of phenolics occurs. Presence of esters of hydroxycinnamic acids plays a prominent role in the browning process thus inducing coupled oxidation reactions to other phenols leading to formation of polymerizing quinones [55]. In ®nished wines, polyphenol oxidation is slower and phenolic content is stabilized. HPLC in conjunction with spectrophotometry in the UV region (250±350 nm) were applied for determining the phenolic fraction. Varietal di€erences in phenolic composition of expressed juices during preparation could be interpreted as being fundamentally due to genetically controlled variability in the distribution of phenolic components within grape berry structure [56]. The values of the selected parameters can be signi®cantly altered from year to year, especially as a consequence of climatic and environmental factors. To obtain results that are valid in the long term, it is necessary to include data obtained from various vintages in order to weigh the parameters according to their variability throughout the years. The analysis of variance of polyphenolic data showed signi®cant di€erences among varieties. Procatechuic acid and trans-coumaric acid varied considerably depending on vintage year, and were used eciently to group varietal grape musts from the PenedeÂs region [5, 57]. The concentration of 15 polyphenols was measured in wines from a range of white (Chardonnay, Riesling, Seyval Blanc, Vidal) and red (Pinot Noir, Cabernet

Sauvignon, Cabernet Franc, Merlot, Gamay Noir) cultivars grown in the viticultural region Niagara. Several signi®cant and characteristic di€erences in the content were identi®ed and relative patterns of individual polyphenols were determined [58]. Moreover, several phenolic compounds, such as epicatechin, tran-caftaric, 2S-glut-caftaric and p-coumaric acid were found to be signi®cantly di€erent according to variety, vintage year and winery for white wines from PenedeÂs region [5]. Phenolic composition of 92 wine vinegars coming from di€erent wines from the south of Spain was determined by HPLC. Phenolic content was shown to e€ectively classify and predict the membership of samples according to employed treatment method or geographical origin of substrate wine [25]. Polyphenolic substrates were used for characterizing cider brandies of di€erent ages made from freshly pressed apple must and from reconstituted apple concentrate. Polyphenolic and furanic compounds were used for distinguishing classes of cider brandies. The main modeling variables thus detected were aromatic aldehydes and syringic acid [59]. Color and ¯avour of red wines is greatly a€ected by anthocyanins. The initial color of red wine is fundamentally due to extraction of anthocyanin pigments from the black grape skins during vini®cation [60]. In addition, these compounds promote formation of complexes with proteins, the formation of complexes with proteins, and oxidative browning. The reactions of anthocyanins with other wine components alters wine color during aging or simply during storage [55]. The rate of decline in the anthocyanin concentration is in¯uenced by factors such as temperature, oxygen access, pH and free SO2 content [56]. Some researchers showed that certain types of anthocyanidins and some ¯avonoids could be used to discriminate wines from different regions [14,61]. Total phenolic content in conjunction with sensory evaluation, was shown to most e€ectively classify French wines in terms of their geographical origin [40]. Changes in phenolic compounds during white wines browning from c.v. Pedro Ximenez and c.v. Baladi grapes were investigated. Caftaric and coumaric acids as well as procyanidins were the compounds displaying the greatest di€erences between wines of these two varieties [21]. The phenolic compounds of ether extracts from dry sherry wines, recently bottled, and from seven commercial brands were analyzed by HPLC. An e€ective differentiation of seven wine brands was e€ected on the basis of only ®ve phenolic compounds [24,62]. In another research, phenolic compounds were related to wine di€erentiation. Polyphenolic content analysis made possible the di€erentiation between nine clones at 100% [63]. Di€erences among the low molecular weight phenolic content of di€erent wines have also been investigated. Important di€erences were found as a function of wine age and employed elaboration method

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and relationships between origin and phenolics were tentatively established [64]. Anthocyanin content used as a main parameter of high enological interest has shown di€erences among Spanish varietal red wines [65]. In another study, 31 wine samples with di€ering production origin and vintage were analysed for total phenolic content and a direct correlation between them was suggested [66]. Table 1 presents some of the most frequently employed phenolic compounds for wine classi®cation according to region/country of origin.

Volatile compounds

In general, volatile analysis is extensively used for varieties characterization. Satisfactory separation of French and American Pinot Noir wines was conducted based on 1-hexanol and cyclohexane. Using purge and trap followed by capillary gas chromatography and mass spectrometry, 19 highly volatile compounds have di€erentiated some white wines of Galicean origin [67]. As far as vinegars are concerned, polyalcohol content is regarded as an origin discriminator. The determination of polyalcohols (xylitol, erythritol, arabitol, mannitol, sorbitol, s-inositol, m-inositol) by capillary gas chromatography-mass spectrometiy, has allowed the discrimination of vinegar raw material at least for white products [39]. Forty-four odor active compounds were quanti®ed in Scheurebe and GewuÈrztraminer wines. Calculation of odor activity values of odorants showed that di€erences in odor pro®les of both varieties were caused by cis-rose oxide in GewuÈrztraminer wine and by 4-mercapto-4methylpeptan-2-one in Scheurebe wine. These compounds are suitable indicators for the determination of ¯avour di€erences, and can lead to wine authentication [68]. The valuable contribution of ethyl esters of fatty acids and acetates of higher alcohols to wine aroma has been known. These compounds are synthesized during must fermentation and are particularly important in young wines [69±71]. The role played by these compounds depends on wine type. In white wines their main role is in the perception of tree fruit and tropical fruit. In rose wines the intensity of tree fruit aroma was correlated to ester content. In red wines these compounds do not determine the intensity of fruit aromas and they play a modulating role on aroma quality [3]. The discrimination of Chardonnay wines from di€erent vintage years

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was heavily based on 38 volatile compounds (alcohols, esters, amides, acids and others) [41]. Two aromatic alcohols (tyrosol and tryptophol) were quanti®ed in 34 red wines from three regions. Simultaneous evaluation and correlation of elements, amino acids and aromatic alcohols, provided an interpretation of the di€erences between wines related to their geographic and varietal origins [61]. Sou¯eros et al. [16] carried out similar experiments classifying 58 French wines originating from regions of Bordeaux, Bourgogne, Alsace and Champagne. Identi®cation of a large number of volatile compounds was performed by linked SPI injector gas chromatography-ion trap mass spectrometiy in Galician wines. Forty of them were quantitatively determined in order to characterize and di€erentiate Galician wines among them. The 13 selected variables for constructing the pro®les of the samples were 3-methyl-2-methylbutanol, ethyl-octanate, nerol, butyric acid-hotrienol, hexyl acetate, 4-methyl-l-pentanol, 2-butoxyethanol, ethyl 2butenoate, hexanoic acid, ethyl butyrate, benzyl alcohol and decanoic acid [67]. The volatiles of various grape varieties have been extensively studied and high levels of alcohols and terpenic oxides have generally been reported for Muskat and some others aromatic cultivars, such as Wisser, GewuÈrztraminer, Riesling and Muller-Thurgau [72]. Furthermore, terpenic alcohols were considered important contributors to the varietal character of the wine [73]. Table 2 depicts some of the most frequently employed volatile compounds for wine classi®cation according to region/country of origin.

Color parameters

Analysis of wine color using tri-stimulus parameters L*, a* and b*, was used for detection of blending in production of rose wines. The blending of wine is authorized in certain well-de®ned circumstances. The problem needs to be addressed as it is one of the primary frauds concerning wines. In France, a distinction exists even between the rose wines and the claret category. Claret wines, standing between red and rose wines, were quite easily ascribed to their category by analysis of color parameters [4, 64, 74, 75]. Moreover, wine color was determined by means of the XYZ coordinates and CIELAB parameters, but not one of them could signi®cantly di€erentiate among vini®cations.

Table 1. Phenolic compounds used for classi®cation of wines

Table 2. Volatile compounds employed for classi®cation of wines

Country

Phenols employed

Country

Volatiles employed

Reference

France Italy Spain Spain

Anthocyanidins Procatechuic acid, trans-coumaric acid Anthocyanidins Coumaric and caftaric acid, procyanidin

France France Germany

Terpenic alcohols Tyrosol and tryptophol 4-Mercapto-4-methlypeptan2-one and cis-rose oxide

[73] [14 61] [68]

Reference [14] [5] [65] [21]

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Discrimination between six di€erent varietal wines was achieved using three standard analytical parameters (L*, a* and C*) with a level of classi®cation [65].

Amino acids

Amino acids in wines originate from various sources. Those indigenous to the grape can be partially or totally metabolized by yeasts during the growth phase, some are excreted by living yeasts at the end of fertilization or released by proteolysis during the autolysis of dead yeasts, whereas others are produced by enzymatic degradation of the grape proteins. It is also well known that the amino acid content of grapes is dependent upon the fertilization and climatic conditions, and on duration of skin maceration in the must [61]. Amino acids were very e€ectively used to characterize varieties for their geographical origin. An increase in amino acid content is very common in sparkling wines because of the overpressure of CO2 in the bottle [1]. Amino acids were used for the di€erentiation of Champagnes and sparkling wines. Champagnes are richer than sparkling wines in all amino acids, except arginine, because of the second fermentation in the bottle and long contact with lees (in a study of 110 wines). Champagnes were successfully separated from sparkling wines by amino acid analysis [13]. In another case, analysis of 20 amino acids resulted in separation of wines from Bourgogne, Bordeaux and Beaujolais [76]. More than 60 wines coming from the regions of Bordeaux, Bourgogne, Alsace and Champagne were classi®ed according to their geographical origin, type and aging by analysis of 21 amino acids, volatiles and biogenic amines [16]. One hundred and sixty musts samples from three Champagne varieties (Pinot Noir, Pinot Meunier, Chardonnay), were separated with amino acids analysis by using gas chromatography. Serine, ornithine, citrulline, arginine and proline, were the employed amino acids for the di€erentiation. However, arginine is one of the genetic characteristics of the varieties, and proline represents the age of the vintage [77]. Free amino acids and ethanolamine were determined for the characterization of Macabeo, Xarel.lo, and Parellada white wines from the PenedeÂs region. Asparagine, proline, lysine were the most popular compounds for distinguishing the varieties according to the geographical origin [5, 57]. In an interesting study, amino acids were used for the di€erentiation of 34 French red wines from three regions. The contents of proline, hydroxyproline, arginine, otnithine, alanine, senine, glycine, valine, leucine, asparagine, threonine, isoleucine, methionine, lysine, tyrosine, phenylalanine, histidine and ethalonamine were measured, but only proline, hydroxyproline and ethanolamine could lead to e€ective di€erentiation [24]. Many other researchers used amino acid analysis to di€erentiate wines according to grape variety used or to production area [18, 41, 48, 78]. Table 3 summarizes some of the most frequently

Table 3. Amino acids employed for wine classi®cations Country

Amino acids employed

Reference

France

Serine, ornithine, citrulline, arginine, proline Total amino acids Total amino acids Asparagine, proline, lysine Total amino acids

[73]

France France Spain France

[61] [76] [5, 57] [19]

employed amino acids for wine classi®cation according to region/country of origin.

Multivariate statistical analysis applied to wine products Introduction to multivariate analysis

In recent years, characterization of wines by means of di€erent analytical parameters and multivariate statistical techniques has received increasing attention. Instrumental analyses in conjunction with multivariate analysis were able to classify wines from di€erent geographical regions and to describe similar and discriminating sensory and chemical characteristics. In sensory research, people use their senses to evaluate certain properties of wines. The members of a sensory panel are usually referred to as assessors. The judgments of the assessors as well as the chemical results of analyses can take di€erent forms and they represent speci®c properties. These properties are called attributes and constitute the variables of the statistical analyses used to assess the results of sensory and chemical studies. Pattern recognition is among the most popular methods for ensuring the distinction of individuals [1]. An individual refers to any sample on which numerous determinations, called variables, are performed. Usually pattern recognition comprises four steps according to which the correlation of data coming from di€erent groups is carried out: . Preprocessing to convert all the data to the same unit to avoid a scale e€ect (a common technique is subtracting every number by the mean of all individuals for a variable and dividing it by the standard deviation). . Selection of the most discriminating variables for classi®cation (number of individuals). . Group classi®cation. . Classi®cation evaluation with test samples that do not participate to the construction of the model. . Identi®cation of unknown samples [I].

When a number of wines are judged on one attribute (e.g. color), an ordinary analysis of variance (ANOVA) can be performed, whereas when several di€erent attributes are used, a multiple ANOVA (MANOVA) must be used [38].

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A set of multivariate statistical methods that recognize the fact that the attributes do not have the same meaning (e.g. in sensory science research, the assessors do not necessarily mean the same thing for an attribute) are the so-called K-sets methods. K-sets methods are GCA and constitute useful methods that allow selection of variables and search for natural groups. CA is used to comprise an unsupervised classi®cation procedure that involves a measurement of either the distance or the similarity between objects to be clustered. Objects are grouped in clusters in terms of their nearness or similarity [79]. The initial assumption is that the nearness of objects in the p-space de®ned by the variables re¯ects the similarity of their properties [80]. PCA identi®es, in the hyperspace of the variables, the directions on which most of the information is retained, thus reducing the dimensionality of the system. By projecting the objects of the data set into the space of the ®rst few components, it is possible to demonstrate di€erences among the various objects, determining at the same time which variables are principally involved [41, 81]. LDA hypothesizes that the distribution is multivariate normal and that the covariance matrix of each category (dispersion of the category) is not signi®cantly di€erent from one case to another. The Mahalanobis distances of each object from the centroids of the categories are computed, the object resulting assigned to the category with lowest distance. The delimiter between two categories is a linear function, e.g., a straight line in the case of two independent variables, a plane for three variables, etc. To check the predictive ability of LDA, the following method has been used: an evaluation set, made of 20% of the objects, randomly selected, has been used to verify the classi®cation rules obtained by the training set [41, 62]. A CDA was performed on well-de®ned groups of samples in order to determine the canonical functions that maximize the separation between groups. Discriminant analysis was used to provide information on the possibility of separating di€erent viticultural regions, de®ned in a multidimensional space, by maximizing the distance between the gravity centers of each group. In order to measure the classi®cation power of the analytical data, the number of individuals correctly predicted to belong to the assigned group can be calculated. This number is expressed as a percentage of the group population. Classi®cation power=(no. of correctly classi®ed individuals/sample population)100 [6] K nearest neighbor (KNN) method as a classi®cation technique is a nonparametric procedure that employs the Euclidean distance for selecting the K nearest objects to the sample to be classi®ed. It does not formulate a hypothesis on distribution of the variables used. Once the K nearest objects are de®ned, the sample

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is classi®ed to the category in which the majority of objects selected belong [59]. The importance of a given feature in making the decisions is proportional to its contribution to the distance calculation [50, 79]. Soft independent modeling of class analogy (SIMCA) is a classi®cation procedure that uses linear discriminant functions derived from disjointed PCA of the data [82]. One set of functions is derived for each category derived by computing the category mean and the speci®ed number of the principal components. Objects are classi®ed into the category whose principal component model best reproduces the data. Only data points belonging to a given category are used in determining the model functions of that category. The importance of each feature in classi®cation is determined by its contribution to the category covariance matrices [50]. Partial least squares (PLS) is an asymmetric method permitting us to predict one set from the other and treats the two sets di€erently. This modeling technique establishes the relationship between two sets of predictor and response variables. It is a correlation analysis that estimates the values of one variable from a set of controllable independent variables [81].

Use of multivariate methods in wine authenticity

A classi®cation of wines according to geographical origin, variety and aging by several multivariate analysis methods is provided in Table 4. PCA and CDA were applied to identify similarities among wines from the same region of origin and main di€erences between wines from di€erent regions, respectively. By applying these methods some researchers managed to classify 22 red wines from four main wine regions in France based on both sensory and chemical analyses (total phenol and color intensity) [40]. Some other studies have also utilized sensory data alone or in conjunction with chemical data, to discriminate wines according to their geographical origin. PCA was used to compare sensory descriptions of Pinot Noir originating from three wine regions in California [83]. The signi®cance levels of di€erences in sensory were determined with ANOVA. The employed two-way classi®cation model with main e€ects and interaction between the wines and the assessors and ®nally showed that all attributes were rated signi®cantly di€erent across the wines. In that, the assessors were regarded as a random e€ect. PCA was used to measure the greatest variation in sensory attributes and chemical compounds among the wines in order to visualize and interpret regional di€erences, whilst CDA was conducted to classify the wines in four di€erent groups according to their region of origin. A total of 81.8% were correctly classi®ed by chemical analysis and 63.6% by sensory analysis. In this study, some aromas common to both groups of wines were shown to be important contributors to the

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Table 4. Classi®cation of wines according to geographical origin, variety and aging by applying several multivariate analysis methods Classi®cation of wines according to:

Determining property/ characteristic

Multivariate analysis

Reference

Geographical origin

1. Instrumental a. Elements K, Na, Ca, Mg, Rb, Li Li, Rb Ba, St, Rb, Li (DH)ow (DH)I (DH)II Na, K, Rb, Cs, Cr, Fe, Co, Zn, Ag Mn, Li B, P Li Lead isotopes Lanthanides

PCA, CDA LDA, KNN, SIMCA ANOVA, CDA CLA MLRA, KNN, SIMCA PCA PCA DA PCA

[61] [50] [6] [92,93] [94] [95] [2] [1] [52]

b. Volatiles Total volatiles Alcohols, acids, esters, terpenes, miscellaneous Total volatiles Decanoic acid Polyalcohol content

PCA PCA, CLA, SIMCA, KNN CLA, DA PCA CLA, MANOVA, LDA

[2] [4,67] [29] [2] [39]

PCA PCA, DA LDA LDA, BPANN

[2] [35] [24] [25]

PCA, CDA

[40]

CLA, DA PCA, CDA

[29] [14,61]

PCA, CDA

[40]

KNN, DA

[11]

PCA PCA, CLA, SIMCA, KNN PCA, DA PCA, SLRA

[27] [4] [65] [3]

ANOVA, PCA

[5]

PCA PCA, ANOVA PCA, DA PCA LDA, BPANN

[27] [5,57] [65] [21] [25]

d. Amino acids Proline, serine, ornithine, citrulline, arginine

PCA

[77]

e. Color parameters L*,a*,b*

PCA, DA

[65]

ANOVA, PCA

[5]

ANOVA, PCA

[5,57]

c. Phenols Procyanidin B2 Anthocyanin, ¯avonol aglycones, glucosides Total phenols (Hydroxymethyl) furaldehyxde, tyrosol, ca€eoyltartaric acid, syringaldehyde, vanillic acid, ca€eic acid, gallic acid ethyl ester, vanilin Total phenols d. Amino acids Proline, total nitrogen Proline, hydroxyproline, ethanolamine, total nitrogen Varieties

2. Sensory evaluation I. Instrumental a. Elements Na, K, Ca, Mg b. Volatiles Hexanol Total volatiles Total volatiles Ethyl butyrate, ethyl hexanoate, ethyl octanoate, ethyl decanoate, ethyl laurate, isobutyl acetate, isomyl acetate, hexyl acetate, phenylethyl acetate c. Phenols s-Glutathionyl-caftaric acid, epicatechin, trans-coumaric acid, trans-ferulic acid, trans-coumaric/trans-caftaric ratio, total caftaric, trans- cis- caftaric acid, coutaric acids Procynidin B3 Total phenols Anthocyanins Coumaric acid, caftaric acid, procyanidin Isoquercetin, coumaroyltartaric acid, (hydroxymethyl)furaldehyde, tyrosol, ca€eolytartaric acid, p-hydroxybenzoic acid, gallic acid, p-coumaric, isoquercetin

Aging

2. Sensory evaluation a. Elements b. Volatiles trans-2-Hexenol, isoamyl acetate, ethyl hexanoate c. Phenols Gallic acid, vanillic acid, ca€eic acid, syringic acid, vanillin syringaldehyde, ferulic acid, coniferyl aldehyde, furfural, p-hydroxybenzaldehyde Total phenols

LDA,KNN,PLS,SIMCA

[59]

LDA

[64]

d. Amino acids Glycine, tyrosine

ANOVA,PCA

[57]

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sensory evaluation, while the chemical intensities for these attributes were low [40]. PCA and DA were also applied for classifying varieties of young red wines produced in the south-east of Spain, in terms of chemical and color parameters. PCA was used as a factor analysis for detecting a logical grouping of variables and an incipient characterization of the varietal wines. In an attempt to improve the results, interpretation a DA was carried out to identify the discriminant function that provided the best classi®cation [65]. Di€erences between wines, according to their concentrations and the nature of the esters, were depicted by PCA. ANOVA indicated that ethyl anthralinate was the most important parameter for e€ective wine di€erentiation. The variation of four esters was interpreted and well correlated with PCA [84]. PCA in conjunction with stepwise linear regression analysis (SLRA) were applied to getting an insight into the fermentation e€ect on the aroma of young Spanish wines. Stepwise discriminant analysis is a method for seeking out subsets of variables most useful to discriminate classes [85]. The PCA diagrams were based exclusively on the chemical information. The sensory information was superimposed after having taken into consideration that wines belonged to di€erent categories. The advantage of this manner of using the PCA is that problems derived from the colinearity between the variables do not exist and the region of the chemical hyperspace containing the wines is reduced through PCA to a smaller dimension. In white wines, highly signi®cant correlations between fruit notes and ester composition of the aroma were obtained by SLRA. Although the behavior of rose and red wines is much more complicated to interpret, employment of the statistical methods provided important hints about the quality. The ratio of ethyl esters to acetates and the total ester content were the variables that better characterized the system [3]. In another work, characterization of Rias Baixas wines was performed applying non-supervised and supervised multivariate methods of analysis (CA and PCA). Di€erentiation of Rias Baixas certi®ed wines was carried out by comparing their volatile pro®le by GSMS (volatiles were expressed as percentage of the total area) to that of other white Galician wines. The separation of wines into classes was based upon one single variable. The ®rst multivariate approach to the data was carried out by CA (Fig. 1) resulting in six clusters of variables and three clusters of samples. By applying PCA, 70% of the initial variance was retained. Four factors were used to establish the di€erences between Rias and non-Rias Baixas wines [4, 67]. The multivariate statistical methods including classi®cation by Euclidean distances from group centroids, were employed to characterize and classify ®ve Venetian white wines, based on chemical analytical results (inorganic components, classical determinations, aroma com-

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Fig. 1. Dendrogram of cluster analysis of wines of Rias Baixas origin (samples AX) and non-Rias Baixas origin (samples BX): A, clustering of samples; B: clustering of variables [4].

pounds). The variables were well-suited to detect and to single-out good multivariate group structures for the ®ve wines. Among the chemical compounds, cis-3-hexen-1-ol has shown a particularly high discriminating power, coupled with stability in time. Both PCA and canonical variate analysis (CVA) were performed giving best results with CVA, while PCA showed poorer eciency in highlighting the group structure [11]. PCA and DA were applied to the results of the analysis of the anthocyanins, ¯avonols, and color parameters in Spanish red wines aged in wood and collected from various wineries in the Ribera de Duero Appellation of Origin region. Pronounced di€erences were found among the wineries within the framework of the general standard established by the appellation of origin. The

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two multivariate analyses were applied to the results of individual components and to the values for certain combinations of components. PCA was applied to the total variables for grouping individual compounds and yielded two components with values greater than unity explaining 85% of the total variance. The distribution of the wines in the plot was de®ned by taking two factors as the coordinates axes. PCA of the individual variables showed that the wineries were distinguishable, with their wines aggregating as aging progressed. While the distribution of the young wines appeared to be somewhat random, the particular aging conditions at each winery imparted speci®c and distinctive attributes of wines, increasing their di€erentiating characteristics as aging advanced. The ®rst factor was associated with the anthocyanins and with variations in the red and yellow color values and thus condensation and oxidation reactions. The second factor was associated with blue color values and with certain ¯avonols and hence provided information on color alternations brought about by co-pigmentation phenomena, which are dependent upon the ¯avonol type and concentration. Discriminant Analysis was used to summarize the results. A plot of the scores in the coordinate plane de®ned by two canonical components of the functions showed the greatest discriminating power for the considered wines [35]. Sixty white wines, identi®ed through 120 analytical variables, were submitted to the analysis of variance (ANOVA) and PCA. ANOVA has shown the mean chemical values of the variables that were found to be signi®cantly di€erent according to variety, vintage year and winery. Some volatiles, higher alcohols and the total phenol and ¯avonoid content were responsible for di€erentiating wines from di€erent vintages. Conventional determinations, organic acids, glucose, fructose, glycerol, amino acids, proteins, volatile compounds, higher alcohols, total phenols and some other chemical compounds were analyzed by PCA. A grouping of the wine samples according to winery was achieved by PCA [5, 57]. An interpretation of di€erences among wines related to their di€erent geographical and varietal origins has been carried out based on the results of statistical analyses. Thirty wine samples were analyzed for elements, amino acids, aromatic alcohols and phenolic compounds. Although several variables have varied signi®cantly across the wine samples for grape varieties and production areas, PCA resulted in by few conclusions. Five di€erent SDA were performed employing, separate, elements and amino acids to discriminate grape varieties or production areas. A satisfactory classi®cation of examined wines was e€ected by employing as few as four variables [61]. Linear discriminant analysis (LDA) is a widespread parametric method of classi®cation purposes and has been applied to establish a possible di€erentiation

among dry sherry wines by capitalizing on phenolics. LDA was applied to obtained chromatographic data. All available variables (seven) were used resulting in a correct classi®cation scheme. After this di€erentiation, the variables, not included in the discriminating function, were eliminated. Then, LDA was performed again only with the variables included in the discriminating functions and an e€ective spatial distribution of the samples was established [24, 62]. LDA was equally applied to assess the role of polyphenolic content for di€erentiation of clones from the Palomino Fino variety [63]. PCA and LDA were employed to discriminate among di€erent vintage years (1986, 1987, 1988) of Chardonnay musts and wines from 31 growing areas in a relatively small region of Trentino in Italy. PCA has been used as a promising classi®cation technique. In musts, four variables (79.3% of the total variance) emerged as signi®cant and according to the double cross-validation. Two of the four eigenvectors were explained with a short percentage of the total variance and did not contain information for a discrimination among the years. The other two eigenvectors have led to a satisfactory separation among the vintages (Fig. 2). The use of LDA as

Fig. 2. Data set of musts scores and loadings (variable index reported) on eigenvectors 2 and 3 [41].

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a classi®cation technique has been suggested and made possible the identi®cation among the covariance matrices of the three categories. A proper stepwise feature selection procedure has been performed as a pretreatment of data aiming at identifying the variables with highest discriminant ability and hence decreasing the number of variables to be used in processing the data, eliminating those variables whose information, though possibly signi®cant for other objectives did not contribute to this speci®c purpose. The discriminant scores were computed by LDA using only six variables. Wines were characterized by 63 variables and the results were obtained using an univariate approach through evaluation of the Fisher weights for all di€erent possible pairs of years. Five variables (64.7% of the total variance) were signi®cant and according to the double cross-validation. A satisfactory separation of di€erent vintages was made possible by two variances and, then, LDA was the technique chosen for classi®cation [41]. Application of multivariate analysis (PCA, CLA, LDA) to phenolics resulted in satisfactory classi®cation of di€erent wine vinegars. The attempts to di€erentiate vinegars were based on the kind of employed raw material [86] or on the process involved [87]. Seven signi®cant factors were used for PCA and with these factors, 76% of total variance was explained. Cluster analysis was applied for searching natural grouping among the samples. Thus, the data matrix was subjected to a hierarchical agglomerative cluster analysis of cases. A dendrogram (tree diagram) was obtained, taking the Euclidean distance as metric and the Ward method as an amalgamation rule. These two methods have assumed knowledge of the number of classes. Some variables were selected for the classi®cation according to manufacture and some others for the classi®cation according to geographical origin with the use of LDA. For the sake of comparison, another dependent classi®cation method based on back propagation arti®cial neural networks (BPANN) [88] was selected. Multivariate analysis was used to determine tentatively unbiased indicators of the geographical origin of wines. One hundred and sixty-®ve grape samples were collected in well-de®ned vineyards of France. The analysis of variance (ANOVA) performed on the di€erent individuals resulted in four groups. The isotopic and elemental variables were highly signi®cant to distinguish must from two or more regions at the 99.9% con®dence level. The main factors contributing to the data set were determined without any prior assumptions by PCA and described in terms of the original variables. PCA was used allowing more than 80% of the initial variance as far as the isotopes were concerned. When the trace elements were considered, ®ve components explained 76% of the total variance. Each value was subtracted from the general mean of the considered variable and then divided by the standard deviation of the variable. The diagonalization

331

for obtaining the eigenvectors and eigenvalues of the new space was performed on the correlation matrix. Varimax and Quartimax rotations were carried out in order to explain the loadings of components in the reduced space with respect to the signi®cant eigenvalues. The isotopic variables have represented the climate of the di€erent region of production, whereas trace element composition was related to the soil. The classi®cation of the wine samples to typical Appellations was e€ected by CDA (Fig. 3) [6]. Changes in phenolic compounds during accelerated browning in white wines from two Spanish varieties were evaluated by using PCA. Two variables produced the best di€erentiation between the wines of the two varieties studied [21]. Several lots of Chardonnay and Grenache blanc grapes were treated with pomace contact and hyperoxidation prior to classi®cation. Variations in the chemical and sensory properties were examined by principal component and factor analyses. PCA was performed on the compositional and browning

Fig. 3. Representation of the wines from four viticulture regions of France in 1990. (a) In the plane of the two ®rst canonical discriminant functions D1 and D2. (b) In the plane of the of the second and the third functions D2 and D3 [6].

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capacity data to illustrate the relationships among the analytical variables and the wines. Three principal components were shown to be the most signi®cant ones accounting together for 87% of the total variance. The PCA of the sensory data showed that pomace contact was bene®cial to Chardonnay wines and detrimental to Grenache wines [89]. Statistical analysis showed that discrimination of vinegar raw material, at least for white products, is possible by employing qualitative and quantitative differences in polyalcohols. The hypothesis that raw material a€ected the polyalcohol content of vinegars was con®rmed by the results of multivariate variance analysis (MANOVA), carried out on all categories of vinegar with at least ®ve samples, regardless of color. Besides MANOVA, CLA was used and similarities in samples were found. Clusters were formed on the basis of raw material, country of origin or the vinegar production ®rm. Both MANOVA and CLA results have shown that the subdivision of the samples on the basis of commercial categories did not always re¯ect di€erences in polyalcohol content. Since the greater di€erences in polyalcohol content were between the vinegars that produced from di€erent raw materials, LDA was used to identify the raw material employed to produce a given vinegar. Finally, the multivariate statistical analysis has shown that polyalcohol content was a€ected by raw material and production techniques while total acidity is not a signi®cant factor for e€ective characterization [39]. Eight di€erent aroma terms, signi®cantly di€ering across three white wines from PenedeÂs, were employed for PCA. The mean terms and Fischer's least signi®cant differences for each term of the sensory evaluation, were calculated by analysis of variance (ANOVA). PCA of the correlation matrix of mean ratings for each sensory term, di€ering across the nine PenedeÂs wines, was used to illustrate the relationship among sensory terms and the relative aromas of the individual wines. The ®rst principal component contrasted the wines high in ¯oral, citrus and black pepper aromas with those high in nutty aroma. The second principal component separated wines higher in tropical fruit and caramel versus those with greater intensity of shoe polish and bell pepper descriptors. Then a stepwise discriminant analysis was constructed on the nine wines to obtain the subset of terms that best revealed di€erences among varieties. Finally, the wines were classi®ed by variety using only the shoe polish and ¯oral terms [5]. The multivariate methods are not performed only in wine authenticity but for general discrimination as well. A perfect example is the discrimination between two forests from either the extractive or ¯avour results. Wood from 20 trees from each of two forests was assessed for ¯avour and chemical composition. ANOVA and PCA showed signi®cant di€erences between the two forests for both varieties. Initial ana-

lyses using both linear and non-linear methods suggested the PCA was most suited to the data, explaining more variation than non-linear methods. The latter assumed a linear model to describe relationships between response variables and principal components. The ®rst analysis has shown how samples from the two forests have been di€erentiated according to some components. The second analysis has investigated the e€ect of time on samples heating. The ®nal analysis was carried out on all samples using scores of the number of times each ¯avour term is used for sample description. The PCA results re¯ected signi®cant di€erences [90]. Vine performance, fruit composition and wine sensory attributes of GewuÈrztraminer in response to vineyard location and canopy manipulation were analyzed with the use of PCA. The experiment was conducted on GewuÈrztraminer vines between 1988 and 1992, of three vineyard sites. Descriptive analysis of aged 1988 wines produced clear site di€erences in cedar aroma, spicy and muscat ¯avour, aftertaste, astringency and body. PCA indicated a clear separation of the three sites (Fig. 4). PCA were performed and successfully separated the various wines [91]. LDA and KNN were used to distinguish cider brandies on the basis of raw material and aging time in casks. The variables stood for furanic and polyphenolic pro®les. Prior to applying multivariate to typify the cider brandies, a univariate analysis was carried out to determine whether any of the variables by themselves could satisfactorily distinguish the categories established.

Fig. 4. Projection of descriptive analysis data (n=24) of aged 1988 wines on PCA factors 2 (!9% of variability) and 3 (16% of variability). Aroma descriptors are denoted by lowercase letters, and ¯avor and tactile descriptors by uppercase letters. Vineyards are indicated by octagons, squares and triangles. Canopy manipulation treatments are denoted by open characters, solid and boldface [91] .

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Consecutively, LDA was used in order to ascertain the most relevant variables for classi®cation purposes. Three variables were selected and 76.4% of total variance was explained within group di€erences. Employment of KNN showed that the overall percentage of hits ranged between 80 and 84%, which is lower than the corresponding percentage obtained by the LDA method. Two modeling methods such as SIMCA and PLS were used. Four components were computed for the two classes that have arisen from KNN classi®cation. Sensitivities and speci®cities were also computed for both classes from a reduced model. Correct classi®cations obtained from SIMCA amounted to 88% and these two modeling variables were adequate for describing both models. PLS was also employed to distinguish spirits on the basis of their aging time in small casks. The variables with the highest modeling power were three (aromatic aldehydes) and the spirits were typi®ed on the basis of their aging time [59]. The BPANN method is often used for classi®cation purposes because it is non-parametric and does not satisfy requirements of linear separation of classes. Classi®cation based on the acidi®cation procedure led to good rates for both LDA and BPANN. Furthermore, BPANN has proved to provide better classi®cation and prediction than LDA on basis of geographical origin, as there was a nonlinear separation among classes of various origins [25]. A simple regression analysis was applied to establish the correlation between the color of wine, its total phenolic content and superoxide radical scavenging activity values [66]. PLS was also applied to distinguish a true rose wine, made from only red grapes and light maceration, from one produced by blending of red and white wine. A model was constructed using the spectra of mixed red and white wines in di€erent proportions that simulated the visual characteristics of true rose wines. The robustness of the model was then tested against wines of known mixed composition and the validity of calculation of the proportion of red and white wine was estimated as prediction error. This prediction error was expressed as the di€erence between predicted and true values [75]. KNN (a statistical method of comparison) and CDA (a multiple discriminant analysis) were used for the analytical di€erentiation of 53 Venetian white wines (Fig. 5). The apparent error rate amounted to 12.3% whereas the expected actual error rate estimated by the jackknife procedure and the learning set/test partition method was about 18%. Therefore, the results obtained by the KNN classi®cation showed that the two methods of pattern recognition had the same classi®cation power [10]. In 42 white wines from Galicia several trace elements were determined and data were processed using CA, PCA, DA, KNNs and soft independent modeling of class analogy to develop a typi®cation of wine samples

333

Fig. 5. Graphical representation of wine groups on CDA. Area 1, Soave 1977 (*), 1980 (*), 1981 ( ); area 2, Prosesco 1977(&), 1979 (&), and 1980 ( ); area 3, Verduzzo 1977 (~), and 1981 (~) [10].

of Rias Baixas origin. Preliminary data analysis by cluster and PCA used the complete data set. LDA, KNN and SIMCA were applied to the complete data in an attempt to discriminate two categories (Rias Baixas wines and non Rias Baixas wines). Then, features, containing the most discriminant information for the classi®cation, were chosen. The criterion used for the selection was Fisher weights which is a qualitative estimate of utility of a given measurement for separating categories. Using lithium and rubidium as key features, a nearly correct classi®cation was e€ected. The 42 wines used as objects were randomly divided between training set and evaluation set. KNN and SIMCA were applied on the basis of only the two selected features [50]. The determination of the authenticity of wine from its trace element composition was examined using the statistical technique of discriminant analysis. This was able to identify the region of origin of Spanish wines from di€erent regions. In order to test the robustness of the data sets, two di€erent statistical packages (Genstat and SPSS) were employed. First, all trace elements were analyzed using the Genstat discriminate program, which is a statistical method for applying the canonical discriminant analysis. This program identi®es linear combinations of the original variables that maximize the ratio of the between group to within group variation. This provides functions of the original variables that can be used to discriminate between groups. The wine data were tested in two di€erent ways. In the ®rst case, three groups of wines were de®ned from three separate Spanish regions. The trace element data for each wine

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were tested against this data set to determine its authenticity. In the second case, two authentic groups were de®ned representing English and Spanish wines. Again, individual wine data were then tested against the data set to determine the country of origin of wine [12]. The second evaluation was undertaken to investigate the minimum number of trace elements needed to separate the three Spanish wine regions (DA, SPSS). Finally it was also used to evaluate whether the country of origin could be ascertained if the analysis was restricted to white wines from England and Spain. The study has strongly indicated that trace element analysis provided an excellent prospect for determining the region of origin of wines [12].

Conclusions

Authenticity of wines is an important but dicult problem that could be solved by appropriate quanti®cation of a variety of constituents. Wine is a complex mixture of organic as well as inorganic compounds. There are many and varying factors in¯uencing the mixture composition. They start in the vineyard to end up in the fermentation wine cellar. These factors are related to oenological environment including ground, climate and the variety of starting vine up to the employed oenological practice. Multivariate analysis has been traditionally employed in food quality evaluation as well as for typifying and characterizing wines or other products. Recently, a number of novel methods and new uses of already established techniques was published showing that some of the newer methods of multivariate data may be most useful for the wine di€erentiation. The occasionally observed behaviour discrepencies of wines could be due to predominance of the varietal character of the wines over the geographical origin. The variables are, frequently, more strictly linked to local conditions such as local temperature, exposure to sunshine and rainfall among others. Winemaking practices (fermentation temperature, yeast, ®ning agents) may greatly a€ect some wine constituents such as some amino acids, volatile compounds and higher alcohols. Di€erences due to climate as well as fermentation conditions and cellar practices, are relevant, and many of the variables used are closely dependent on those factors. Wine components such as polyphenols are genetically in¯uenced and it is possible to correctly characterize wines according to their variety. The veri®cation of wine authenticity may lay in the use of combined techniques such as analysis of isotopes and minerals. The pro®le is unique to a speci®c wine and can be safely used as an authenticity proof. Of course, several other instrumental determinations in combination with sensory evaluation are useful and may lead to accurate classi®cations of unknown wines but they cannot stand on their own as secure methods of autheticity.

Finally, a global strategy for determining the origin of a completely unknown wine should comprise the following three stages: 1. Lead isotope ratio determination to locate the wine origin by continent 2. 18O or site speci®c deuterium for the determination of latitude, north or south. 3. Metal determination such as lithium, rubidium or manganese for classi®cation to the sub-region.

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