Multivariate analysis for the classification and differentiation of Madeira wines according to main grape varieties

In order to differentiate and characterize Madeira wines according to main grape varieties, the volatile composition (higher alcohols, fatty acids, ethyl esters and carbonyl compounds) was determined for 36 monovarietal Madeira wine samples elaborated from Boal, Malvazia, Sercial and Verdelho white grape varieties. The study was carried out by headspace solid-phase microextraction technique (HS-SPME), in dynamic mode, coupled with gas chromatography–mass spectrometry (GC–MS). Corrected peak area data for 42 analytes from the above mentioned chemical groups was used for statistical purposes. Principal component analysis (PCA) was applied in order to determine the main sources of variability present in the data sets and to establish the relation between samples (objects) and volatile compounds (variables). The data obtained by GC–MS shows that the most important contributions to the differentiation of Boal wines are benzyl alcohol and (E)-hex-3-en-1-ol. Ethyl octadecanoate, (Z)-hex-3-en-1-ol and benzoic acid are the major contributions in Malvazia wines and 2-methylpropan-1-ol is associated to Sercial wines. Verdelho wines are most correlated with 5-(ethoxymethyl)-furfural, nonanone and cis-9-ethyldecenoate. A 96.4% of prediction ability was obtained by the application of stepwise linear discriminant analysis (SLDA) using the 19 variables that maximise the variance of the initial data set.     

Comparative study of artificial neural network and multivariate methods to classify Spanish DO rose wines

Classical multivariate analysis techniques such as factor analysis and stepwise linear discriminant analysis and artificial neural networks method (ANN) have been applied to the classification of Spanish denomination of origin (DO) rose wines according to their geographical origin. Seventy commercial rose wines from four different Spanish DO (Ribera del Duero, Rioja, Valdepeñas and La Mancha) and two successive vintages were studied. Nineteen different variables were measured in these wines. The stepwise linear discriminant analyses (SLDA) model selected 10 variables obtaining a global percentage of correct classification of 98.8% and of global prediction of 97.3%. The ANN model selected seven variables, five of which were also selected by the SLDA model, and it gave a 100% of correct classification for training and prediction. So, both models can be considered satisfactory and acceptable, being the selected variables useful to classify and differentiate these wines by their origin. Furthermore, the casual index analysis gave information that can be easily explained from an enological point of view.     

Metal content in southern Spain wines and their classification according to origin and ageing

Young and aged wines from two viticole zones in the Andalusian province of Córdoba (southern Spain) were analysed for their content in Ca, Mg, Fe, Cu, Mn and Zn by flame atomic absorption spectrophotometry, and Na, K, Al and Sr by flame atomic emission spectrophotometry. Significant differences in mean content were found for Na, Mn, Mg, Fe and Zn between wines from Montilla–Moriles and Villaviciosa. Linear discriminant analysis using those variables gave 97.9% recognition ability and 95.7% prediction ability. Cluster and principal component analysis show some differences in wines according to geographical origin and to the ageing of wines. Significant differences between young and aged wines were found in the mean content for Mg, K, Sr, Zn and Mn, obtaining 93.62% recognition ability and prediction ability by using linear discriminant analysis and leave-one-out cross-validation test, respectively. Finally, linear discriminant analysis could also be able to classify the samples according to their provenance and to their ageing simultaneously, obtaining 93.6% of the wines correctly classified.     

Geographical classification of some Australian wines by discriminant analysis using HPLC with UV and chemiluminescence detection

HPLC with UV and acidified potassium permanganate chemiluminescence detection, combined with multivariate data analysis techniques, were used for the geographical classification of some Australian red (Cabernet Sauvignon) and white (Chardonnay) wines from two regions (Coonawarra and Geelong). Identification of the wine constituents prominent in the chromatography was performed by mass spectrometry. Principal components analysis and linear discriminant analysis were used to classify the wines according to region of production. Separation between regions was achieved with both detection systems and key components leading to discrimination of the wines were identified. Using two principal components, linear discriminant analysis with UV detection correctly classified 100% of the Chardonnay wines and, overall 91% of the Cabernet Sauvignon wines. With acidified potassium permanganate chemiluminescence detection, 75% of the Chardonnay wines and 94% of the Cabernet Sauvignon wines were correctly classified using two factors.     

Identification of anthocyanins in wines by liquid chromatography, liquid chromatography-mass spectrometry and nuclear magnetic resonance

Liquid chromatography (LC) was used for the fractionation of particular anthocyanins in glycoside form from methanol extracts of red grape skins and solid phase extracts of red wine. By the combination of nuclear magnetic resonance spectroscopy and LC-mass spectroscopy the identification of 13 anthocyanins in a particular LC fraction and hence the in particular peaks in chromatograms were obtained. Peaks areas in the chromatograms obtained under the semi-quantitaive conditions of the solid phase extracts of red wines Pinot Noir, Cabernet Sauvignon and Merlot from the Coastal wine-growing region in Slovenia, produced in 1999, were used as input data in chemometric analysis. The chemometric methods used were hierarchical clustering analysis and regularised discriminant analysis. The results of both methods give 100% correct classification of wines regarding the vine variety.     

Changes in wine volatile compounds of varietal wines during ageing in wood barrels

Twelve single-variety wines were aged in new American oak barrels. A control wine of each variety without ageing in wood was also studied. The volatile aroma composition of these wines was analysed after the following combinations of time in wood and time in bottle: 0 months in wood plus 18 months in bottle, 4 months in wood plus 14 months in bottle, 9 months in wood plus 12 months in barrel and 12 months in barrel plus 6 months in bottle.The data obtained were studied by two different multivariate methods: factor analysis by principal components and stepwise linear discriminant analysis. The results obtained showed that the capacity of wine to extract the volatile compounds from the wood depended on the variety of grape used. Furthermore, it was also found that the changes in the volatile composition of the wines induced by the wood ageing were more important during the first 4 months. In addition, the volatile compounds studied allowed to differentiate and correctly classify the wines according to the time they had been in contact with the wood.     

Varietal discrimination of Australian wines by means of mid-infrared spectroscopy and multivariate analysis

This study outlines the use of mid-infrared (MIR) spectroscopy combined with principal component analysis (PCA) and linear discriminant analysis (LDA) for the varietal classification of commercial red and white table wines. Three red varieties (Cabernet Sauvignon, Shiraz and Merlot) and four white varieties (Chardonnay, Riesling, Sauvignon Blanc and Viognier) were sourced from different wine regions in Australia. Wine samples were scanned in transmission on a FOSS WineScan FT 120 from wave numbers 926 to 5012 cm⁻¹. All samples were sourced from the 2006 vintage and had not been blended with any other variety or wine from other regions. Spectral data were reduced to a small number of principal components (PCs) and LDA was then performed to successfully separate the wines into the different varieties. To test the robustness of the LDA models developed for the red wines, a set of red wines scanned in 2005 were used. Correct classification of over 95% was achieved for the validation set.     

Multivariate classification of rosé wines from different Spanish protected designations of origin

Wine composition depends on many factors which are especially important for quality wines from specific regions, such as protected designation of origin (PDO) wines. Nineteen analytical parameters were analysed in commercial rosé wines from different Spanish PDOs (Ribera del Duero (RD), Rioja (Rj), Valdepeñas and La Mancha (M-V)), and from two consecutive vintages. Stepwise linear discriminant analysis (SLDA) was used to differentiate and classify these wines by their geographical origin. All the wines were made from “similar” varieties of grapes. The final model selected 12 variables, being ethanol and calcium the most important variables for the differentiation of the three PDOs. The evaluation of the model was done by crossvalidation, obtaining a global percentage of correct classification of 98.8% and of global prediction of 97.3%. These results can be considered satisfactory and acceptable, and the selected variables can be useful to differentiate these wines by their origin.     

Identification of the Varietal and Regional Origin of Red Wines by Classification Analysis

We examined 144 samples of red dry wines of varieties Cabernet and Merlot, produced in the territory of the main wine-making enterprises of the geographical zones of Krasnodar krai, to consider the possibility of determining the regional and varietal identity of red wines. The concentration of micro- and macroelements in the wines was determined by inductively coupled plasma atomic emission spectrometry. Chemometric studies were performed in the Statistica package environment, using discriminant analysis and classification trees. Adequate mathematical models were proposed for wines to identify the grape variety and the region where it grows. A software product was developed that automates the necessary calculations.     

Classification of commercial wines from the Canary Islands (Spain) by chemometric techniques using metallic contents

Eleven elements, K, Na, Ca, Mg, Fe, Cu, Zn, Mn, Sr, Li and Rb, were determined in dry and sweet wines bearing the denominations of origin of El Hierro, La Palma and Lanzarote islands (Canary Islands, Spain). Analyses were performed by flame atomic absorption spectrophotometry, with the exceptions of lithium and rubidium for which flame atomic emission spectrophotometry was used. Sweet wines from La Palma were elaborated as naturally sweet with over-ripe grapes and significant differences were found in all the analysed elements with the exceptions of sodium, iron and rubidium with regard to dry wines from the same island. Contrarily, sweet wines from Lanzarote elaborated with grapes in a similar ripening state to dry wines did not present significant differences between them with the exception of strontium, the content of which was greater in dry wines. Among the three islands, significant differences in mean content were found with the exceptions of iron and copper. Cluster analysis and principal component analysis show differences in wines according to the island of origin and the ripening state of the grapes. Linear discriminant analysis using rubidium, sodium, manganese and strontium, the four most discriminant elements, gave 100% recognition ability and 95.6% prediction ability. The sensitivity and specificity obtained using soft independent modelling of class analogy (SIMCA) as a modelling multivariate technique were both 100% for El Hierro and Lanzarote, and 100 and 95%, respectively, for La Palma. The modelling and discriminant capacities of the different metals were also studied.     

Vinegar classification based on feature extraction and selection from headspace solid-phase microextraction/gas chromatography volatile analyses: a feasibility study

Headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography (GC) and multivariate data analysis were applied to classify different vinegar types (white and red, balsamic, sherry and cider vinegars) on the basis of their volatile composition. The collected chromatographic signals were analysed using the stepwise linear discriminant analysis (SLDA) method, thus simultaneously performing feature selection and classification. Several options, more or less restrictive according to the final number of considered categories, were explored in order to identify the one that afforded highest discrimination ability. The simplicity and effectiveness of the classification methodology proposed in the present study (all the samples were correctly classified and predicted by cross-validation) are promising and encourage the feasibility of using a similar strategy to evaluate the quality and origin of vinegar samples in a reliable, fast, reproducible and cost-efficient way in routine applications. The high quality results obtained were even more remarkable considering the reduced number of discriminant variables finally selected by the stepwise procedure. The use of only 14 peaks enabled differentiation between cider, balsamic, sherry and wine vinegars, whereas only 3 variables were selected to discriminate between red (RW) and white wine (WW) vinegars. The subsequent identification by gas chromatography-mass spectrometry (GC-MS) of the volatile compounds associated with the discriminant peaks selected in the classification process served to interpret their chemical significance.     

Quantitative determination of free and hydrolytically liberated beta-damascenone in red grapes and wines using a stable isotope dilution assay.

Quantitative analysis of free and hydrolytically liberated beta-damascenone in grapes and wines was developed, using a stable isotope dilution assay. Free beta-damascenone was isolated from grapes and wines by diethyl ether-hexane (1:1, v/v) extraction and the precursor(s) (glycosidic, polyols) of beta-damascenone using Sep-Pak Plus C18 RP cartridges. Hydrolytically liberated beta-damascenone was generated by acid hydrolysis from the precursor(s) extract. Red wines from Bordeaux (Merlot, Cabernet Sauvignon and Cabernet Franc, 1995 and 1996 vintage), Burgundy (Pinot Noir, 1995 and 1996 vintage) regions and Grenache wines from Chateauneuf du Pape and C?tes du Rh?ne (1995 vintage) were analysed to quantify free beta-damascenone. The wines made from Grenache and Cabernet Sauvignon (1996 vintage) grapes presented the highest mean amounts of free beta-damascenone, 5.4 and 5.5 micrograms l-1, respectively. Merlot, Cabernet Sauvignon and Cabernet Franc grapes of Bordeaux (1996 vintage) and their corresponding wines were analysed for quantification of free and hydrolytically liberated beta-damascenone. The levels of hydrolytically liberated beta-damascenone in grapes could predict closely the levels of free beta-damascenone in the corresponding wines after one year of ageing, i.e., almost half the levels found for the grape samples. The influence of enzyme and heat treatment of Merlot wine samples on their beta-damascenone levels was studied. Heat treatment doubled the levels of this compound, but enzyme treatment generated, in the corresponding wines, half the levels of beta-damascenone found in the non-enzyme treated wines.     

Brand-dependent volatile fingerprinting of Italian wines from Valpolicella

Head-space solid-phase microextraction (HS-SPME) coupled to gas-chromatography-mass spectrometry was developed and applied to obtain the volatile aromatic fingerprints of three typical Italian wines, Valpolicella, Amarone and Recioto, all produced in the restricted geographical area of Valpolicella (Veneto, Italy) with the same grape cultivars within the regulations of a rigid disciplinary of production. Differences between the three typologies are mainly linked to the different withering times to which grapes are subjected before vinification, which strongly influences the concentration and the development of volatile aroma compounds. A total of 22 different wines (7 Valpolicella, 10 Amarone and 5 Recioto) were characterised in terms of aromatic volatile profile with the aim to distinguish the different products and to evaluate the possibility to differentiate the same product from different brands. For the chemometric evaluation of the data one-way analysis of variance (ANOVA), principal component analysis (PCA) and hierarchical cluster analysis (HCA) were tested. All the chemometric tools employed allow to differentiate between the three products. More intriguing is the ability of the chemometric approach to differentiate between the same product (Amarone, Recioto) from different winery, thus showing the potential of this approach to characterize the brand-dependent typicality of wines, which is usually related to subtle technological differences which nevertheless have strong influences on the organoleptic characteristics of the products.     

A useful approach for the differentiation of wines according to geographical origin based on global volatile patterns

In this study, the feasibility of solid‐phase extraction combined with gas chromatography and mass spectrometry in tandem with partial least squares discriminant analysis was evaluated as a useful strategy to differentiate wines according to geographical origin (Azores, Canary and Madeira Islands) and types (white, red and fortified wine) based on their global volatile patterns. For this purpose, 34 monovarietal wines from these three wine grape‐growing regions were investigated, combining the high throughput extraction efficiency of the solid‐phase extraction procedure with the separation and identification ability. The partial least squares discriminant analysis results suggested that Madeira wines could be clearly discriminated from Azores and Canary wines. Madeira wines are mainly characterized by 2‐ethylhexan‐1‐ol, 3,5,5‐trimethylhexan‐1‐ol, ethyl 2‐methylbutanoate, ethyl dl ‐2‐hydroxycaproate, decanoic acid, 3‐methylbutanoic acid, and (E)‐whiskey lactone, whereas 3‐ethoxypropan‐1‐ol, 1‐octen‐3‐ol, (Z)‐3‐hexenyl butanoate, 4‐(methylthio)‐1‐butanol, ethyl 3‐hydroxybutanoate, isoamyl lactate, 4‐methylphenol, γ‐octalactone and 4‐(methylthio)‐1‐butanol, are mainly associated with Azores and Canary wines. The data obtained in this study revealed that solid‐phase extraction combined with gas chromatography and quadrupole mass spectrometry data and partial least squares discriminant analysis provides a suitable tool to discriminate wines, both in terms of geographical origin as well as wine type and vintage.     

Usefulness of chemometrics and mass spectrometry-based electronic nose to classify Australian white wines by their varietal origin

A combination of mass spectrometry-based electronic nose (MS e_nose) and chemometrics was explored to classify two Australian white wines according to their varietal origin namely Riesling and unwooded Chardonnay. The MS e_nose data were analysed using principal components analysis (PCA), discriminant partial least squares (DPLS) and linear discriminant analysis (LDA) applied to principal components scores and validated using full cross validation (leave one out). DPLS gave the highest levels of correct classification for both varieties (>90%). LDA classified correctly 73% of unwooded Chardonnay and 82% of Riesling wines. Even though the conventional analysis provides fundamental information about the volatile compounds present in the wine, the MS e_nose method has a series of advantages over conventional analytical techniques due to simplicity of the sample-preparation and reduced time of analysis and might be considered as a more convenient choice for routine process control in an industrial environment. The work reported here is a feasibility study and requires further development with considerably more commercial samples of different varieties. Further studies are needed in order to improve the calibration specificity, accuracy and robustness, and to extend the discrimination to other wine varieties or blends.     

Classification of Nebbiolo-based wines from Piedmont (Italy) by means of solid-phase microextraction-gas chromatography-mass spectrometry of volatile compounds

Sixty-eight samples of wines from Piedmont (Italy) were analysed to determine their content of volatile compounds, using the solid-phase microextraction (SPME) technique coupled with gas chromatography-mass spectrometry (GC-MS). Samples were from five groups of wines: Barolo, Barbaresco, Nebbiolo d'Alba, Roero and Langhe Nebbiolo, all produced from the Nebbiolo grape in the Langhe and Roero areas (province of Cuneo, Piedmont) but differing in vintage (respectively, 3 years, 2 years, 1 year, 8 months and few months) and production zone. Thirty-five analytes were identified; peak area data, corrected for internal standard, were used for pattern recognition treatments. Principal components analysis, hierarchical cluster analysis, Kohonen self organising map, stepwise linear discriminant analysis and soft independent modelling of class analogy were applied to the data, revealing a good separation between the five groups. A main factor, strictly connected to wine vintage, was identified and found to be related to some analytes.     

Application of LC–MS and tristimulus colorimetry to assess the ageing aptitude of Syrah wine in the Condado de Huelva D.O. (Spain), a typical warm climate region

The study of the evolutions of different wine pigment families, copigmentation/polymerisation processes and colour characteristics during the first year of ageing in oak barrel has allowed the assessment of the ageing aptitude of Syrah wines from “Condado de Huelva D.O.”, a warm climate region. A total of 32 anthocyanic pigments were identified, including 14 major compounds from grape and 18 minor derivatives formed during the vinification. The anthocyanin profile changed towards more chemical complexity, being vitisin-like pyranoanthocyanins the predominant minor pigments during the first month of ageing. As wine became older, a progressive increase on the content of 4-vinylcatechin, 4-vinylphenol and 4-vinylcatechol compounds took place. Results showed that copigmentation occurred during the whole process of ageing inducing visual perceptible colour effects. Simultaneously to the copigmentation decrease, the degree of polymerisation increased during ageing, being maximum at 9 months old wines (77%). The colour of wines evolved progressively in a positive way from 3 to 9 months of ageing, becoming darker and with more vivid colour. However, from 9 to 12 months of ageing, the chemical structure of wines was negatively affected resulting in lighter, with more red-orange hues and less vivid colours. The inclusion of the chemical and colorimetric information on the PCA model allows us to reach very good discriminations among the Syrah wines with different wood contact period.     

Headspace solid-phase microextraction–gas chromatography–mass spectrometry for profiling free volatile compounds in Cabernet Sauvignon grapes and wines

The complex aroma of wine is derived from many sources, with grape-derived components being responsible for the varietal character. The ability to monitor grape aroma compounds would allow for better understanding of how vineyard practices and winemaking processes influence the final volatile composition of the wine. Here, we describe a procedure using GC–MS combined with headspace solid-phase microextraction (HS-SPME) for profiling the free volatile compounds in Cabernet Sauvignon grapes. Different sample preparation (SPME fiber type, extraction time, extraction temperature and dilution solvent) and GC–MS conditions were evaluated to optimize the method. For the final method, grape skins were homogenized with water and 8 ml of sample were placed in a 20 ml headspace vial with addition of NaCl; a polydimethylsiloxane SPME fiber was used for extraction at 40 °C for 30 min with continuous stirring. Using this method, 27 flavor compounds were monitored and used to profile the free volatile components in Cabernet Sauvignon grapes at different maturity levels. Ten compounds from the grapes, including 2-phenylethanol and β-damascenone, were also identified in the corresponding wines. Using this procedure it is possible to follow selected volatiles through the winemaking process.     

Multivariate analysis of the polyphenol composition of Tempranillo and Graciano red wines

Vitis vinifera L. cv Graciano is often used as a blending partner of Tempranillo based wines because it is considered to contribute significantly to the quality. The aim of this study is to discriminate between Tempranillo and Graciano monovarietal wines, and those made by the incorporation of 20% of Graciano variety in two different stages (at the end of malolactic fermentation and mixing the two grape varieties in the pre-fermentative maceration stage) of the winemaking process of the Tempranillo variety. To achieve this, supervised and unsupervised pattern recognition tools were applied to the data obtained in the study of the detailed polyphenolic composition, colour and other oenological parameters (143 variables). Patterns related to stages in the winemaking and ageing process, different wines and vintages can be observed using principal component analyses. Furthermore, linear discriminant analysis has been applied in order to characterise the wine samples. From the 143 variables, flavan-3-ols have exerted a profound influence on wine differentiation.