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Characterization of wines using

compositional profiles andchemometricsJavier Saurina

This review discusses strategies for characterizing wines based on compo-

sitional profiles as sources of information. Contents of low molecular organic

acids, volatile species, polyphenols, amino acids, biogenic amines and

inorganic species seem to depend on climatic, agricultural and wine-making

factors. As a result, compositional profiles of these families of natural winecomponents can be exploited as potential descriptors of wine and its quality.

Most characterization studies rely on chemometrics to facilitate extraction

of information. Cluster analysis, principal component analysis and related

methods are currently used for discrimination, classification, modeling and

correlation.

2010 Elsevier Ltd. All rights reserved.

Keywords: Chemometrics; Classification; Cluster analysis; Compositional fingerprint;

Discrimination; Instrumental assay; Modeling; Principal component analysis; Sensory

property; Wine characterization

1. Introduction

In recent years, consumers have been

increasingly interested in information on

the characteristics and the quality of food

products [1]. A wide variety of analytical

methods have been published for charac-

terizing products (e.g., wine, honey, tea,

olive oil and juices) [2]. In the case of

wines, meticulous controls are required to

assess factors (e.g., geographical origins,

grape varieties, vintages and oenological

practices) as a way of evaluating quality

and detecting fraudulent adulterations[3].

Consumer preferences in the selection ofwines take into consideration several

factors (e.g., pleasant color, taste and aroma,

ecological production, guaranteed origin

and quality). These characteristics cannot

be described in a simple manner from

given individual compounds (or various)

of the sample but they result from complex

combinations of hundreds of components.

Typical tests to estimate wine quality rely

on sensorial assays carried out by a group

of expert panelists [4]. Although these

methods are probably the best option for

checking a small number of samples, they

have limitations when dealing with large

batches of wines (e.g., cost of assays, the

inter-individual variability in perception ofsensations and the unreliability of human

senses due to fatigue, health condition or

environmental interferences).

In order to overcome the limitations of

expert-panelist assays, some research

groups have pointed out the possibility of

correlating sensory characteristics with

physic-chemical variables [5,6]. Charac-

terization studies can therefore be carried

out in a simple, sensitive and reproducible

way using suitable analytical methods,

often in combination with chemometric

treatment of data[7]. Wine properties can

be assessed from the contents of organic

constituents, the elemental composition of

metals or isotopic analysis [8]. For ex-

ample, fraudulent practices (e.g., addition

of sugars or low-quality wines during fer-

mentation) can be detected from isotopic

analysis by nuclear magnetic resonance

(NMR) and mass spectrometry (MS) [9].

These methods rely on measurements of

isotopic ratios of 13C/12C, 15N/14N,

18O/16O and 2H/1H, which depend on

geographical origin and vintage. Well-defined authentic samples are needed as

references to establish the ranges of ratios

of genuine samples and to detect those

that are anomalous or different.

Isotopic analysis seems to be complex

and expensive for routine control of food

products, so simpler, cheaper and faster

analytical methods to satisfy demands on

wine analysis are welcome. In this way,

spectroscopic, spectrometric and separa-

tion methods have been proposed as being

Javier Saurina*

Department of AnalyticalChemistry, University of

Barcelona, Diagonal 647,

08028-Barcelona, Spain

*Tel.: +34 934 039 778;

Fax: +34 934 021 233;

E-mail:[email protected]

Trends Trends in Analytical Chemistry, Vol. 29, No. 3, 2010

234 0165-9936/$ - see front matter 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.trac.2009.11.008
mailto:[email protected]://dx.doi.org/10.1016/j.trac.2009.11.008http://dx.doi.org/10.1016/j.trac.2009.11.008mailto:[email protected]


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more straightforward for wine characterization [13].

Elemental analysis is mainly applied to the study of

origins and authenticities[9]. Gas chromatography (GC)

and MS are successful in varietal characterization. There

is special interest in MS combined with headspace

devices as electronic noses (e-noses), which have great

impact in evaluating aroma properties [6]. High-performance liquid chromatography (HPLC) performs

excellently in winemaking and vintage assays, and

characterizing sensorial properties of color and taste.

The following sections describe the most significant

achievements in wine characterization using chemo-

metric analysis of physic-chemical data. The methods

discussed come from representative papers published in

the last 10 years. Section 2 comments on the principal

types of data as sources of analytical information and

briefly refers to the chemometric methods utilized.

Section 3 contains analytical applications to wine anal-

ysis (andTable 1 provides a summary).

2. Data analysis

The type of data handled in wine characterization is,

in general, multivariate in nature. Data comprise a list

or array of values, so-called first-order data in che-

mometric terminology [10,11]. As shown in Fig. 1,

concentrations of organic and inorganic species,

instrumental responses, including spectra or chro-

matographic profiles, or more complex combinations of

data from various sources can be used as analytical

data to extract relevant information. Some commentsabout these data are as follows.

2.1. Concentration of wine components

Discrete concentration values of each desired species

result in a rich, simple source of data (Fig. 1a), so the

information is principally recovered from selected com-

ponents while irrelevant species do not influence the

study. The use of concentration values obviously re-

quires prior quantification, which is often complex and

time consuming. At this stage, uncontrolled calibration

errors affecting quantification may confound character-

ization studies and may lead to misinterpretation of re-sults.

2.2. Instrumental responses

Raw or preprocessed instrumental signals comprising

spectra and chromatographic profiles can be directly

used as chemical fingerprints of the corresponding

samples (Fig. 1b). Note that the use of such signals

makes the quantification step unnecessary. Besides, in

this strategy, identification and full separation of all

components occurring in the samples is not a funda-

mental issue. Although concentrations of components

are not explicitly known they are implicit in the inten-

sities of the signal features.

This approach is gaining popularity because it is so

simple, since, as mentioned, those time-consuming steps

devoted to resolving and to quantifying the desired

analytes can be avoided [12].

Other unspecific instrumental responses (e.g., fromelectronic noses and tongues) have demonstrated great

performance for modeling and estimating sensorial

properties of wines[13].

2.3. Combined data

Data from various analytical techniques can be com-

bined together as a way of enriching the quantity and

the quality of information and arriving at more feasible

conclusions. Hence, amounts of a wide range of con-

stituents and/or data from one or various instrumental

techniques can be brought together in a convenient

manner (see Fig. 1c). As shown in Section 3, various

applications have been reported joining physic-chemicaldata (e.g., pH, conductivity and acidity), concentrations

of relevant species, and chromatographic, MS and other

instrumental responses.

The simultaneous study of properties of a series of

wines is carried out using a table of data in which each

row corresponds to a given sample (Fig. 1f). In this

arrangement, dimensions arem n,m being the number

of samples and n the number of measurements of each

sample.

2.4. Chemometric methods

Principal component analysis (PCA) has been used inmost recent exploratory studies of wines. Information

gained from PCA is often complemented with informa-

tion from other methods [e.g., groups of similar wines

can be assessed by cluster analysis (CA)]. Also, classifi-

cation of wines into pre-established categories or groups

is often performed by linear discriminant analysis (LDA)

and Soft Independent Modeling of Class Analogy

(SIMCA) methods. Artificial neural networks (ANNs)

and partial least squares (PLS) regression are sometimes

used for correlation purposes (e.g., in assessing rela-

tionships of physic-chemical variables with oenological

and sensorial attributes). PCA, CA, LDA, ANN and PLS

methods are detailed elsewhere [14].

3. Wine studies

3.1. Elemental analysis

The composition of trace and ultratrace mineral

elements of wines seems to be an excellent source of

information to be exploited in characterization studies

[15]. Grapes receive trace elements from the soil, so that

their elemental composition is reasonably correlated

with the geological characteristics of the producing

Trends in Analytical Chemistry, Vol. 29, No. 3, 2010 Trends

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areas. Mineral elements are not metabolized or trans-

formed during oenological processes and they remain

almost unaltered from musts to wines. The classification

of samples according to geographical regions or

denomination of origins and the detection of certain

adulterations may therefore rely on elemental composi-

tion. The mean contents of trace elements also dependson winemaking practices.

In the 1990s, the determination of trace elements in

wines was based mainly on flame atomic spectroscopies.

The absorption mode was, in general, sufficiently sensi-

tive to quantify transition metals (e.g., Fe, Cu, Ca, Co,

and Mn), often present at mg/L levels. Similarly, flame

emission spectroscopy was utilized for determining some

alkaline metals (e.g., Li, Na and K) and alkaline earth

metals (e.g., Ca and Sr).

More recently, the introduction of highly sensitive

atomic spectroscopies to wine analysis has expanded the

variety of elements quantified. Graphite furnace atomic

absorption spectrometry (GF-AAS), inductively coupledplasma optical emission spectroscopy (ICP-OES) and

inductively coupled plasma mass spectrometry (ICP-MS)

have been used to determine elements occurring at ng/L

levels in wines.

Apart from atomic spectroscopies, X-ray FL and elec-

trophoresis have been used to determine the elemental

composition of wines.

Table 1 summarizes recent applications.

3.2. Molecular spectroscopies

Ultraviolet visible (UV-Vis), near-infrared (NIR), mid-

infrared (MIR), fluorescence (FL) and NMR spectrosco-pies have been exploited in wine analysis (see Table 1).

UV-Vis spectra contain information regarding the

absorbing moieties, basically, polyphenolic compounds

[29]. The simplest compounds (e.g., phenolic, benzoic and

hydroxycinnamic acids) display absorption bands in the

range 300400 nm. More complex molecules (e.g., stil-

benes, flavanols, flavonols and anthocyanins) also absorb

moderate or strongly in the visible range. The composition

of these compounds is therefore the principal factor

modulating the color of wines and some taste character-

istics. Hence, significant relationships between spectral

data and oenological features can be assessed. Often, UV-

Vis spectral data arecomplemented with measurements in

the NIR range 8002500 nm. The richer source of

information resulting from these enlarged data sets has

been used in various classification studies[31].

FL of wines comes from naturally occurring fluoro-

phores, mainly polyphenols. In general, excitation

spectra have a maximum at 260270 nm and emission

spectra display characteristic features around 370 nm

and 315 nm. The shapes of excitation and emission

spectra vary slightly among wines because of differences

in the composition of native FL compounds. Table 1

includes applications based on FL.

MIR (4004000 cm1) comprises a complex spectral

range containing multiple vibration bands from func-

tional groups of major wine components, including

sugars and polysaccharides, polyphenols, tannins and

organic acids [38]. Currently, spectra obtained with a

Fourier-transform IR (FTIR) spectrophotometer operat-

ing under reflection or transmission modes are utilizedfor classification and quantification.

NMR is excellent for generating characteristic finger-

prints reflecting the complexity of compositional profiles

of wines[46,47]. The main 1H NMR signals are associ-

ated with major components including ethanol, organic

acids, sugars and amino acids. Data can be utilized to

estimate concentrations of alcohols and organic-acid

constituents [48].

From the point of view of information, UV-Vis-NIR and

FL spectra contain a limited number of spectral features,

so limiting their possibilities in discrimination. Despite

this apparent shortcoming, UV-Vis-NIR and FL tech-

niques offer advantages (e.g., simplicity, robustness,availability and minimal sample treatment).

By contrast, MIR and NMR spectroscopies provide

characteristic chemical fingerprints of great interest for

differentiation. However, drawbacks may arise (e.g.,

more tedious sample treatment procedures, more com-

plex measurements and less reproducible results).

3.3. Mass spectrometry

An emerging trend in wine analysis relies on MS for

describing complex aroma properties associated with

volatile components, which comprise hundreds of sub-

stances (e.g., alcohols, aldehydes, esters, acids, andterpenes). Some of them are already present in grapes,

while others are formed during fermentation and ageing.

Indeed, alcoholic fermentation is the vinification step

leading to formation of the greatest number of aroma

components. Further, ageing involves multiple aroma-

maturation processes (e.g., oxygenation and reactions

with molecules extracted from the oak barrels). In gen-

eral, the concentration of volatile compounds decreases

with ageing and strongly conditions the final flavor of

wines.

Contents of volatile compounds in wines vary by

several orders of magnitude, from major to trace con-

stituents. Compounds (e.g., ethanol and glycerol) occur

at concentrations of g/L. Ethyl acetate, isoamyl alcohol,

ethyl lactate, among others, are present at levels in the

range 10100 mg/L. There is a long list of components

present at concentrations of mg/L or below.

Methods reported in the literature (see Table 1) gen-

erally combine headspace systems for releasing the vol-

atile species with MS for analytical detection. This

approach is therefore recognized as a type of e-nose, in

which the mass spectra are utilized as fingerprints for

each sample with information related to aroma features.

The chemometric comparison of spectral data is then


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Table 1. Recent methods for the characterization of wines

Samples Characterizationstudies

Instrumentaltechniques

Data analyzed Chemometricmethods

Remarks Ref.

Galician(Spanish) wines

Recognition/discrimination ofprotecteddesignations oforigin. Detection ofadulterations

Flame AESFlame AAS

Li, Rb, Na, K,Mn, Fe, Ca

PCA, CA, LDA,KNN, SIMCA

Most discriminatingelements: Li, Rb and Fe

[16,17]

Oloroso sherry Classificationaccording toprovenance

Flame AESFlame AAS

Na, K, Al, SrCa, Mg, Fe, Cu, Mn, Zn

PCA, LDA Most discriminatingelements: Na, Mg, Fe, Aland Sr. 93% predictionability

[18]

Canary Islandswines

Classificationaccording todenomination oforigins

Flame AESFlame AAS

K, Na, Li, Rb, SrCa, Mg, Fe, Cu, Zn, Mn

PCA, CA, LDA,SIMCA

Significant differences incontents of Fe and Cuamong islands.Most discriminatingelements: Rb, Na, Mnand Sr. 95% predictionability

[19]

Serbian wines Classification of redand white wines

GF-AAS Cu, Mn, Fe, Cd, Pb PCA, FA, CA Discrimination betweenred and white wines wasimpossible

[20]

Sparkling wines Discriminationbetween cava(Catalonian) andchampagne wines

GF-AASHG-AASICP-OES

Cd, Ni, PbAsAl, Ba, Ca, Cu, Fe, K,Mg, Mn, Na, P, Sr, Zn

LDA, SIMCA Remarkableauthentication power.Zero false positives andnegatives

[21]

Valencia(Spanish) wines

Recognition/discrimination ofprotecteddesignations oforigin

ICP-OES AL, Ba, Be, Ca, Cd, Ce,Co, Cr, Cu, Dy, Er, Eu,Fe, Gd, Ho, K, La, Li, Lu,Mg, Mn, Mo, Na, Nd,Ni, Pb, Pr, Sc, Se, Sm, Sr,Tb, Ti, Tm, V, Y, Yb, Zn

PCA, CA Li and Mg contents werecharacteristic of Utiel-Requena and Jumillaregions

[22]

Bohemian(CzechRepublic) wines

Identification oforigins of Bohemianwines

ICP-OES ICP-MS Na, K, Ca, Fe, MgAl, As, Ba, Ce, Co, Cr,Cs, Cu, Li, Mn, Mo, Ni,Pb, Rb, Sb, Sr, Th, U, V,Y, Zn

PCA, DA Best identificationsbased on contents of Al,Ba, Ca, Co, K, Li, Mg,Mn, Mo, Rb, Sr, V andSr/Ba, Sr/Ca, Sr/Mg ratios

[23]

Canary Islandswines

Classification intored, rose and whitewines

ICP-MS Te, Re, Be, Cd, Sn, Sb,Co, As, V, Ni, Ti, Cu, Pt,U, Cs, La, Zr, As, Zn, Rb,Tl, Tb, Ce, Pr, Nd, W,Pb, Sr, Ba, Ho, Tm, Sm,Eu, Gd, Lu, Dy, Er, Yb,Th

LDA, ANN Most discriminatingelements: Sr, Rb, Pb, Be,Ba, Tl, Ti, and Au.Prediction ability > 95%

[24]

German wines Classificationaccording togrowing areas

ICP-MS Li, Be, Ti, Co, Ni, Ga,As, Rb, Y, Zr, Nb, Mo,Cd, b, Te, Cs, La, Ce, Pr,Nd, Sm, Eu, Gd, Tb, Dy,Ho, Er, Tm, Yb, Lu, W,

Tl, U

PCA, CA Prediction ability: 88% [25]

Nebbiolo winesfrom Piedmont(Italy)

Classificationaccording toprovenance into fivegrowing areas

ICP-MS Al, As, B, Ba, Be, Ca, Cd,Ce, Co, Cr, Cs, Cu, Dy,Er, Eu, Fe, Ga, Gd, Ge,Hf, Ho, I, K, La, Li, Mg,Mn, Mo, Na, Nb, Nd,Ni, P, Pb, Pd, Pr, Rb, Rh,Sb, Si, Sm, Sn, Sr, Tb, Te,Th, Ti, Tl, Tm, U, V, W,Y, Yb, Zn Zr

PCA, LDA Most discriminatingelements:Si, Mg, Ti, Mn and Mo

[26]

(continued on next page)




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Table 1 (continued)




Remarks Ref.

Ribera Sacra(Spanish) wines

Discrimination ofRibera Sacra winesfrom other Galician

wines

CE Na, K, Ca, Mn, Li PCA, LDA,KNN, SIMCA

Good categorizationaccording to origin

[27]

CabernetSauvignonwines

Differentiation ofwines produced invarious countries

X-rayfluorescence

K, Ca, Mn, Fe, Ni, Cu,Zn, Br, Rb, Sr

PCA, CA Most discriminatingelements:K, Fe and Rb

[28]

La Mancha(Spanish) wines

Classificationaccording toproducing area

UVVisspectroscopy

Absorption spectra: 300800 nm

PCA, SIMCA 90% prediction abilityaccording to origin. 75%prediction abilityaccording to grapevariety and ageing

[30]

Tempranillowines

Geographicclassification intoSpanish andAustralian groups

Vis and NIRspectroscopy


PCA, LDA, PLS Classification ability >70%

[32]

Riesling wines Preliminaryclassification

according to country



PCA, LDA, PLS Classification ability >67%

[33]

Australian whitewines

Classificationaccording to grapevarieties



PCA, PCR, DA-PLS

Classification ability >96%

[34]

Australian redwines

Assessment ofmodels betweenwine scores andspectra



PLS Ability for predictingsensory quality score:r = 0.61

[35]

Loire Valley(French) grapes

Study of ripening ofFrench Cabernetgrapes. Prediction ofanthocyanincontents

Front-facefluorescence

Excitation spectra ofgrape skins: 250350 nmat kem350 nm

PCA, DA, PLS Classification accordingto ripening.Prediction of malvidin-3-O-glucoside, totalanthocyanins and totalphenols: r > 0.8

[36]

French andGerman wines

Discriminationbetween French andGerman wines

Front-facefluorescence

Emission spectra: 275450 nm at kex261 nmExcitation spectra: 250350 nm at kem350 nm

PCA, DA Classification abilities >93%

[37]

Portuguesewhite wines

Classificationaccording to varietyand winemakingprocedure.Prediction ofmannose contents

FTIR (mid)spectroscopy

FTIR spectra of the winepolysaccharide dryExtracts: 1200800 cm1

PCA, CA, PLS Identification of thewinemaking process ofmust clarification and/ormaceration. Predictionability of mannose, r >0.97

[39]

Red, white andmodel wines

Classificationaccording to tanninlevels. Prediction oftannin contents


FTIR transmissionspectra:400 (or 650)4000 cm1

DA, SIMCA, PLS Classification abilities >60%Prediction ability oftannins, r > 0.96

[40,41]

Austrian redwines

Discriminationamong varieties


Attenuated totalreflectance spectra: 7004000 cm1

CA, SIMCA Classification ability >95%

[42]

Australian redand white wines

Discriminationamong varieties.Fingerprintauthentication


FTIR transmissionspectra:9265012 cm1

PCA, LDA Classification ability >95%

[43,44]

Gamay wines Discriminationamong origin andvintage



PCA, PLS Classification ability >70% (for origin) and >50% (for vintage)

[45]

Bordeaux(French) wines

Discriminationamong growingareas, cultivars andsoil types

1H NMRspectroscopy(500 MHz)

1D 1H NMR spectra(0.78.8 ppm) of driedtissue extracts D2O usingTSP as shift marker

PCA, PLS Significant clusteringaccording to growingareas. Effect of vintagemore noticeable thaneffect of soil type

[46,47]





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Table 1(continued)




Remarks Ref.

Red, roseandwhite wines

Exploratory analysis.Quantification ofwine components

from 1H NMR data

1H NMRspectroscopy(400 MHz)

1H NMR spectra (0.56 ppm) of wines in D2Ousing TSP as shift marker

PLS Prediction abilities of ethanol, glycerol, lacticacid, methanol and

malic acid: r = 0.120.96

[48]

Spanish wines Differentiation andclassificationaccording to origin,variety and ageing

Headspace-MS(e-nose)

MS by EI ionization at70 eV inm/zrange 50200

PCA, SIMCA More difficult to separateaccording to ageing

[49]

Australian whitewines

Classificationaccording to variety(Chardonnay andRiesling)


MS by EI ionization at70 eV in m/zrange 50180

PCA, LDA, PLS Classification abilityaccording to variety >73%

[50]

Australian redwines

Monitoring winespoilage



PCA, LDA, PLS Successful classificationusing 4-ethylphenol asindex of spoilage

[51]

AustralianRiesling wines

Prediction of aromaproperties



180

PCA, PLS Prediction abilities ofscores of aroma notes:

estery (r = 0.75),perfume floral (r = 0.89),lemon (r = 0.82), stewedapple (r = 0.82), passionfruit (r = 0.67) and honey(r = 0.90)

[52]

Italian wines Classification ofwine varietiesaccording to aroma(volatile) fingerprints



PCA Prediction ability >83.3%

[53]

White wines Classificationaccording to originand variety

Differentialpulsevoltammetry:PEDT-modifiedelectrodes

Voltammogram range:0.10.7 V

PCA, PLS Sample clustering withrespect to varieties andorigin

[54]

Italian wines Classificationaccording to originand variety

Headspace thin-filmmultisensoryarrayOthers

Gas sensor e-nose:current resistance of fourdifferent sensorspH, Conductivity andethanol content

PCA, ANN Prediction ability > 78% [55]

Croatian redwines

Influence ofpolyphenoliccomposition onsensory perceptions

RP HPLC-UV Major polyphenols PCA Significant correlationbetween polyphenolsand astringency,bitterness and overallsensory impression

[59]

Lacrima diMorro dAlbared wines

Classificationaccording topolyphenols.Influence on sensoryperceptions

RP HPLC-UV(DAS)RP HPLC-MS(ion trap)

Major polyphenols PCA, FA, PLS Significant correlationbetween anthocyaninconcentrations andpigmentation, sour tasteand astringency

[60]

Greek red wines Differentiationbased on variety andorigin

RP HPLC-UV(DAS)

19 polyphenols DA Sample clustering withrespect to varieties andgeographical origin

[61,62]

South Africanred and whitewines

Classificationaccording to grapevariety

RP HPLC-UV(DAS)

22 major polyphenols ANOVA, PCA,LDA

Classification ability intored and white groups >97%

[63]

Slovenian wines Classificationaccording to variety

RP HPLC-UVRP HPLC-MS

13 anthocyanins ANOVA, CA,PCA, LDA

Classification ability100%

[64]

Fino sherry Study of browning RP HPLC-UV Polyphenols PCA, CA Discrimination betweennatural and acceleratedbrowning

[65]

German redwines

Classificationaccording to variety

RP HPLC-UV 9 anthocyanins PCA, CA Sample clustering withrespect to varieties

[66]





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Table 1 (continued)




Remarks Ref.

Red, rose andwhite Spanishwines

Classificationaccording to type ofwine

RP HPLC-UV,precolumnderivatization

with Dabsyl-Cl

9 biogenic amines PCA, CA Most discriminatingvariables: putrescine,histamine and tyramine

[67]

Tokaji Aszu(Hungarian)wines

Authentication ofwine specialtiesmade frombotrytized grapes

RP-HPLC-UV(DAS)RP-HPLC-fluorimetry (kex345 nm, kem455 nm), post-columnderivatizationwith OPA

Tartaric, malic, shikimic,lactic, acetic, citric andfumaric acids13 biogenic amines

PCA, LDA Identification ability ofauthentic samples >96%

[68,69]

Spanish redwines

Classificationaccording to ageing

RP HPLC-UV(DAS),precolumnderivatizationwith NQS

8 biogenic aminesRaw chromatographicprofiles

PCA, PLS Sample clustering withrespect to ageing. Gooddifferentiation amongyoung and aged wines

[12]

Hungarian redand white wines

Classificationaccording towinemakingtechnology

Ion-exchangeHPLC-UV, post-columnderivatizationwith ninhydrin

8 biogenic amines and20 amino acids

PCA, LDA Classification ability>65% (origin), >62%(variety) and >73%(vintage)

[70]

Pinotage wines Classificationaccording to vintageand origin

SBSE-(headspace) andGC-MS

39 volatile compounds ANOVA, PCA,FA

Reasonable clusteringwith respect to originand vintage

[72]

South Africanred and whitewines

Classificationaccording to cultivar

SBSE-(headspace) andGC-MS

Major volatile and semi-volatile components

PCA, CA, DA Successful classificationof wines according tovariety

[73]

Spanish wines Differentiation ofcertified brands oforigins

Headspace-SPME and GC-FID

18 major volatilecomponents

PCA, LDA, ANN Classification ability100% with respect toorigins using ANN

[74]

Rioja (Spanish)wines

Classificationaccording towinemaking

Headspace-SPME GC-MS

11 major volatilecomponents

PCA, CA, LDA Most discriminatingvariables: 3-methyl-butylacetate, ethyloctanoate,diethylsuccinate,hexanoic acid, 2-phenylethanol anddecanoic acid.Prediction ability 100%

[75]

Andalusian(Spanish) sweetwines

Classificationaccording to grapevariety

Headspace- andGC-FID

>30 volatile components PCA, CA, LDA Prediction ability 100% [76]

Slovakian wines Classificationaccording to variety

Headspace-SPME GC-FID

65 volatile components PCA, CA, ANN,LDA, KNN

Classification ability >95%

[77]

Greekdry redwines

Prediction of sensoryscores from volatilecomponents

Headspace-SPME and GC-MS

GC fingerprint profile PCA, PLS Reasonable relationshipbetween sensoryproperties and GCprofiles

[78]

Madeira wines Characterization ofaroma profiles offive varieties

Headspace-SPME and SBSEand GC-MS

Major aromacomponents

PCA Most discriminatingvariables among dry andsweet wines: cis-oaklactone, diethylsuccinateand ethyl octanoate

[79]

CabernetSauvignonwines

Classificationaccording to origin

Microchip CE-electrochemicaldetection

Electrophoretic profiles PCA, LDA Preliminary insight toclassification

[81]

Hungarianwines

Classification intored and wine classes

Overpressured-thin layerchromatography

Polyphenols andbiogenic amines

PCA, CA Sample clustering withrespect to type of wine

[82]





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Table 1(continued)




Remarks Ref.

South Africanwines

Classificationaccording to variety

LLE and GC-FIDFTIR (mid)spectroscopy

Volatile componentsFTIR transmissionspectra:

9295011 cm1

ANOVA, PCA,LDA

White wines: predictionability >98% using FTIRdata

Red wines: predictionability >86% usingFTIR + GC data

[83]

Italian wines Classificationaccording to origin

Variousanalyticaltechniques

pH, conductivity,acidity, ethanol, totalextract, redox potential,absorbance at fourwavelengths, e-tongueand e-nose profiles

CAIMAN Preliminary assessmentof a new classificationmethod

[84]

Italian wines Authentication oforigin

AAS, HPLC 35 chemical descriptors(polyphenols, metals,organic acids,alcohols, . . .)

SIMCA, UNEQ Most discriminatingvariables:Cu, Zn, anthocyans andSO2

[85]

Spanish (rose)wines


Variousanalytical

techniques

19 chemical descriptors(polyphenols, metals,

classical oenologicalparameters, colorparameters)

LDA, ANN Prediction ability 100% [86]

Greek wines Classificationaccording to origin

HPLCCG-MSAAS

Polyphenols, metals,other oenologicalparameters, sensory data

PCA Satisfactory classificationaccording to origin

[87]

Gamay wines Classificationaccording to origin

FTIR (mid)spectroscopyVariousanalyticaltechniques


pH, acidity, organicacids, phenols, colorparameters

DA Higher performance of MIR spectra with respectto classical oenologicalparameters

[88]

Slovenian wines Study ofchaptalizationpractices.


SNIF-NMRIRMS

D/H isotopic ratios andd13C

PCA, CA, KNN Possibility of detectingadulterations

[89]

Canary Islands(Spanish) wines

Comparison ofwines and musts


Acidity, pH, reducingsugars, B, Ca, ashes, Cu,density, Fe, alcohol, K,Mg, Na, tartaric acid, Pb,SO2, Zn

PCA, LDA, FA Differentiation betweenwines and musts

[90]

Galician(Spanish) wines

Authentication of aGalician certifiedbrand of origin


34 chemical variablesincluding metals,organic acids, volatilecompounds,polyphenols

PCA, CA, LDA,KNN, SIMCA

Most discriminatingvariables:Fe, Li, Rb, delphinidinand epicatechin

[91]

Slovenian andItalian wines


Ion exclusion-HPLCICP-OES1H-NMR

Metals, organic acids,amino acids

PCA, CA Higher performance of NMR spectra comparedwith other data

[92]

Rioja (Spanish)wines

Classificationaccording to winetype (claret, roseandblend)

UV-VisspectroscopyColorimetry

Color parameters ANN, SIMCA Classification ability >50%

[93]

AAS, Atomic absorption spectroscopy; AES, Atomic emission spectroscopy; GF, Graphite furnace; HG, Hydride generation; ICP, Inductively coupled plasma;OES, Optical emission spectroscopy; MS, Mass spectrometry; UV, Ultraviolet; Vis, Visible; NIR, Near infrared; FTIR, Fourier transform infrared; HPLC, Highperformance liquid chromatography; GC, Gas chromatography; CE, Capillary electrophoresis; EI, Electronic impact; DAS, Diode array spectrophotometer; kex,Excitation wavelength; kem, Emission wavelength; FID, Flame-ionization detector; SBSE, Stir-bar sorptive extraction; SPME, Solid-phase microextraction; LLE,Liquid-liquid extraction; SNIF, Site-specific natural-isotope fractionation; NMR, Nuclear magnetic resonance; IRMS, Isotope-ratio mass spectrometry; TSP,(Trimethyl)-propionic-2,2,3,3-d4 acid; PEDT, Poly(3,4-ethylenedioxythiophene); Dabsyl-Cl, (4-Dimethylamino-azobenzene-4-sulphonyl chloride); OPA, o-Phthaldialdehyde; NQS, 1,2-Naphthoquinone-4-sulfonate; PCA, Principal component analysis; CA, Cluster analysis; DA, Discriminant analysis; LDA, Lineardiscriminant analysis; KNN,K-Nearest neighbors; SIMCA, Soft independent modeling of class analogy; ANN, Artificial neural networks; FA, Factor analysis;PLS, Partial least square (regression); CAIMAN, Classification and influence matrix analysis; UNEQ, Unequal class modeling; r, Coefficient of correlation.




9/12

exploited to differentiate and to classify wines according

to variety and other oenological features.

3.4. Electrochemical techniques

To date, electroanalytical data have had little signifi-

cance in characterizing food products. Table 1contains

some preliminary studies for evaluating the possibilities

of these techniques in wine analysis.

3.5. Separation techniques

Separation techniques, especially HPLC and GC, have

been widely used for wine characterization and classifi-

cation. Data of different degrees of complexity (e.g.,

analyte concentrations, peak areas and chromatograms)

can be generated and processed.

In the case of concentrations, the range of analytes

considered is limited to identifiable and quantifiable

components. For peak-area data, identification and

quantification can be omitted. Hence, areas of charac-

teristic peaks at well-defined retention times are taken for

analysis.

In the case of chromatographic profiles, the whole run

or selected time windows can be used as analytical data

[12].

Fig. 2shows an example to illustrate these possibilities

with a chromatogram of the separation of polyphenols

in a red wine by reversed-phase HPLC with UV detection.

As in this case, the great complexity of the wine sample

hinders full separation of all components of interest and

multiple unknown peaks may eventually appear in the

chromatograms. In these circumstances, the separation

of all components and the identification of all chro-

matographic features may not be fully achieved. How-

ever, the occurrence of unresolved peaks should not be

250 300

0

10

20

30

40

50

250 300

mAU

0

10

20

30

40

50

Spectrum

Wine 1

Wine 2

Wine 3

Wine m

Responses 1 to n

Wines

1tom

D(m x n)

Responses 1 to n

Analyte

1

Analyte

2

Analyte

3

Concentrations

(a)

(b)

(d)

(c) Concentrations Spectrum Chromatogram Others

...

Figure 1. Types of data of interest in wine characterization. (a) Array from discrete concentration (or physico-chemical) values from the analysisof a wine. (b) Instrumental (spectral) data profile. (c) Combined array from concentrations and instrumental data. (d) Scheme for constructing adata matrix to be used in the study of a series of wines.




10/12

taken as a negative issue, since chromatograms may be

exploited as chemical fingerprints that provide specific

information on the samples analyzed.

The two principal families of wine components inves-

tigated by HPLC comprise polyphenols and amino com-

pounds (biogenic amines and amino acids).

Compositional profiles regarding polyphenolic and/or

amino species have been correlated with significant

factors (e.g., organoleptic properties, winemaking prac-

tices and grape varieties) [56,57]. However, extracting

information on the influence of oenological factors may

be difficult due to the multifactorial nature of the pro-

cesses involved [58]. Here, again, application of

chemometrics may assist in reaching reliable conclu-

sions.Table 1 provides some representative examples of

relevant HPLC applications.

GC is typically utilized for the analysis of the volatile

fraction of wines. Headspace and solid-phase microex-

traction (SPME) have been extensively used for wine

pretreatment in order to recover volatile components. In

the headspace technique, species are volatilized from the

sample under controlled experimental conditions (either

in static or dynamic mode) and the gas phase is directly

transferred to the chromatograph using an appropriate

interface[71]. Alternatively, gas-phase components can

be adsorbed on a fiber to be further introduced into the

injection port of the GC instrument to run the separa-

tion. In SPME mode, the fiber is immersed in the wine

phase to retain the desired components. In all cases,

analytes are further released from the fibers by thermal

desorption. The chromatographic detection is often

accomplished by MS as a way of enhancing the sensi-

tivity and the selectivity of data. As mentioned in

Table 1, compositional profiles of volatile compounds

have been exploited to extract information on chemical

and sensory features (e.g., taste and aroma).

To date, CE has been of limited significance in wine

analysis, and only a few methods have been proposed for

quantifying wine components[80]. In one example, the

performance of a CE-microchip device with electrochemical

0 5 10 15 20 25

0

100

200

300

400

gallicacid

3,4-dihydroxybenzoicacid

Absorbance(mAU)

Time (min)

catechine

4-hydroxybenzoicacid

vanillicacid

caffeicacid

syringicacid

coumaricacid

ferulicacid

t-resveratrol

Peak

areas

Concentrations

Chromatographic

profile

(Absorbancevstime)

a1

a2

a4 a

5...a

3

c1

c2 c

3 c

5

calibrations

...Figure 2. Possibilities of generating data of different complexity: chromatographic profile (absorbance vs. time), peak areas and concentrations.




11/12

detection was assessed for preliminary classification of

wines[81].

3.6. Combined data sets

The combination of data from various analytical sources

is a powerful strategy to enrich the information content,

thus improving characterization results. For example,physico-chemical parameters, analyte concentrations,

spectra, and chromatograms can be analyzed together

from an extended data set. Note that, in these cases,

differences in the nature and the magnitude of the data

handled have to be compensated for. Typically, nor-

malization or standardization pretreatments are required

to provide similar weight to all variables. Table 1com-

ments on some relevant applications based on combined

data sets.

4. Conclusions

Characterization of wines based on analytical methods

combined with chemometric treatment of data

provides excellent robustness and efficiency, and re-

duces costs compared with sensory tests by expert

panelists. Physico-chemical parameters, concentrations

of wine components and instrumental signals can be

used as multivariate data. Wine features, including

origin, variety and winemaking practices, can be

evaluated from this source of information.

Because of the multiparametric nature of wines,

chemometics makes interpretation of data more feasible.

Contents of mineral elements result in appropriate

descriptors of origin and authenticity. The volatile frac-

tion of wines is useful for categorizing wines based on

aroma characteristics. Polyphenolic fingerprints can be

exploited in analyzing varieties and evaluating color and

taste. Finally, amino compounds are suitable markers for

studying winemaking practices and ageing of wines.

Acknowledgement

This work was supported by the Spanish Ministerio de

Ciencia y Tecnolog a, project CTQ2008-04776/BQU.

References[1] D.M.A.M. Luykx, S.M. Van Ruth, Food Chem. 107 (2008) 897.[2] L.M. Reid, C.P. ODonnell, G. Downey, Trends Food Sci. Technol.

17 (2006) 344.

[3] I.S. Arvanitoyannis, M.N. Katsota, E.P. Psarra, E.H. Soufleros, S.

Kallithraka, Trends Food Sci. Technol. 10 (1999) 321.

[4] N. Cayot, Food Chem. 101 (2007) 154.

[5] S. Lopez-Feria, S. Cardenas, M. Valcarcel, Trends Anal. Chem. 27

(2008) 794.

[6] B. Plutowska, W. Wardencki, Food Chem. 101 (2007) 845.

[7] M. Kozak, C.H. Scaman, J. Sci. Food Agric. 88 (2008) 1115.

[8] M. Gishen, R.G. Dambergs, D. Cozzolino, Aust. J. Grape Wine Res.

11 (2005) 296.

[9] N. Ogrinc, I.J. Kosir, J.E. Spangenberg, J. Kidric, Anal. Bioanal.

Chem. 376 (2003) 424.

[10] S. Sentellas, J. Saurina, S. Hernandez-Cassou, M.T. Galceran, L.

Puignou, J. Chromatogr., A 909 (2001) 259.

[11] S. Sentellas, J. Saurina, J. Sep. Sci. 26 (2003) 1395.

[12] N. Garca-Villar, S. Hernandez-Cassou, J. Saurina, J. Agric. Food

Chem. 55 (2007) 7453.

[13] M. Scampicchio, D. Ballabio, A. Arecchi, S.M. Cosio, S. Mannino,

Microchim. Acta 163 (2008) 11.

[14] S.D. Brown, R. Tauler, B. Walczak, Comprehensive Chemometrics,

Chemical and Biochemical Data Analysis, Vol. 3, Elsevier,

Amsterdam, The Netherlands, 2009.

[15] M. Suhaj, M. Korenovska, Acta Aliment. 34 (2005) 393.

[16] M.J. Latorre, C. Garca Jares, B. Medina, C. Herrero, J. Agric. Food

Chem. 42 (1994) 1451.

[17] R.M. Pena, M.J. Latorre, S. Garca, A.M. Botana, C. Herrero, J. Sci.

Food Anal. 79 (1999) 2052.

[18] P. Paneque, M.T. Alvarez-Sotomayor, I.A. Gomeza, Food Chem.

117 (2009) 302.

[19] S. Fras, J.E. Conde, J.J. Rodrguez-Bencomo, F. Garca-Montelon-

go, J.P. Perez-Trujillo, Talanta 59 (2003) 33 5.

[20] S. Razic, D. Cokesa, S. Sremac, J. Serb. Chem. Soc. 72 (2007)

1487.

[21] A. Jos, I. Moreno, A.G. Gonzalez, G. Repetto, A.M. Camean,

Talanta 63 (2004) 377.

[22] A. Gonzalvez, A. Llorens, M.L. Cervera, S. Armenta, M. de la

Guardia, Food Chem. 112 (2009) 26.

[23] J. Sperkova, M. Suchanek, Food Chem. 93 (2005) 659.

[24] J.P. Perez-Trujillo, M . Barbaste, B. Medina, Anal. Lett. 36 (2003)

679.

[25] G. Thiel, G. Geisler, I. Blechschmidt, K. Danzer, Anal. Bioanal.

Chem. 378 (2004) 1630.

[26] E. Marengo, M. Aceto, Food Chem. 81 (2003) 621.

[27] M. Nunez, R.M. Pena, C. Herrero, S. Garca-Martn, Analusis 28

(2000) 432.

[28] S.J. Haswell, A.D. Walmsley, J. Anal. Atom. Spectrom. 13 (1998) 131.

[29] V. Cheynier, Am. J. Clin. Nutr. 81 (2005) 223S.

[30] M. Urbano, M.D. Luque de Castro, P.M. Perez, J. Garca-Olmo,

M.A. Gomez-Nieto, Food Chem. 97 (2006) 166.

[31] D. Cozzolino, R.G. Dambergs, L. Janik, W.U. Cynkar, M. Gishen, J.

Near Infrared Spectrosc. 14 (2006) 279.

[32] L. Liu, D. Cozzolino, W.U. Cynkar, M. Gishen, C.B. Colby, J. Agric.

Food Chem. 54 (2006) 6754.

[33] L. Liu, D. Cozzolino, W.U. Cynkar, R.G. Dambergs, L. Janik,

B.K. ONeill, C.B. Colb, M. Gishen, Food Chem. 106 (2008) 781.

[34] D. Cozzolino, H.E. Smyth, M. Gishen, J. Agric. Food Chem. 51

(2003) 7703.

[35] D. Cozzolino, G. Cowey, K.A. Lattey, P. Godden, W.U. Cynkar, R.G.

Dambergs, L. Janik, M. Gishen, Anal. Bioanal. Chem. 391 (2008)

975.

[36] M. Le Moigne, E. Dufour, D. Bertrand, C. Maury, D. Seraphin, F.

Jourjon, Anal. Chim. Acta 621 (2008) 8.

[37] E. Dufour, A. Letort, A. Laguet, A. Lebecque, J.N. Serra, Anal.

Chim. Acta 563 (2006) 292.

[38] X.Y. Niu, Y.B. Ying, H.Y. Yu, L.J. Xie, X.P. Fu, Spectrosc. Spec.Anal. 28 (2008) 804.

[39] M.A. Coimbra, F. Goncalves, A.S. Barros, I. Delgadillo, J. Agric.

FoodChem.50 (2002) 3405.

[40] K. Fernandez, E. Agosin, J. Agric. Food Chem. 55 (2007)

7294.

[41] K. Fernandez, X. Labarca, E. Bordeu, A. Guesalaga, E. Agosin,

Appl. Spectrosc. 61 (2007) 1163.

[42] A. Edelmann, J. Diewok, K.C. Schuster, B. Lendl, J. Agric. Food

Chem. 49 (2001) 1139.

[43] C.J. Bevin, A.J. Fergusson, W.B. Perry, L.J. Janik, D. Cozzolino, J.

Agric. Food Chem. 54 (2006) 9713.

[44] C.J. Bevin, R.G. Dambergs, A.J. Fergusson, D. Cozzolino, Anal.

Chim. Acta 621 (2008) 19.




12/12

[45] D. Picque, T. Cattenoz, G. Corrieu, J.L. Berger, Sci. Aliments 25

(2005) 207.

[46] G.E. Pereira, J.P. Gaudillere, C. Van Leeuwen, G. Hilbert, O.

Lavialle, M. Maucourt, C. Deborde, A. Moing, D. Rolin, J. Agric.

Food Chem. 53 (2005) 6382.

[47] G.E. Pereira, J.P. Gaudillere, C. Van Leeuwen, G. Hilbert, M.

Maucourt, C. Deborde, A. Moing, D. Rolin, J. Int. Sci. Vigne Vin 41

(2007) 103.

[48] F.H. Larsen, F. van den Berg, S.B. Engelsen, J. Chemom. 20

(2006) 198.

[49] M.P. Mart, O. Busto, J. Guasch, J. Chromatogr., A 1057 (2004)

211.

[50] D. Cozzolino, H.E. Smyth, W. Cynkar, R.G. Dambergs, M. Gishen,

Talanta 68 (2005) 382.

[51] W. Cynkar, D. Cozzolino, B. Dambergs, L. Janik, M. Gishen, Sens.

Actuators B 124 (2007) 167.

[52] D. Cozzolino, H.E. Smyth, W. Cynkar, L. Janik, R.G. Dambergs, M.

Gishen, Anal. Chim. Acta 621 (2008) 2.

[53] C. Armanino, M.C. Casolino, M. Casale, M. Forina, Anal. Chim.

Acta 614 (2008) 134.

[54] L. Pigani, G. Foca, K. Ionescu, V. Martina, A. Ulrici, F. Terzi, M.

Vignali, C. Zanardi, R. Seeber, Anal. Chim. Acta 614 (2008) 213.

[55] M. Penza, G. Cassano, Food Chem. 86 (2004) 283.

[56] D.P. Makris, S. Kallithraka, P. Kefalas, J. Food Comp. Anal. 19

(2006) 396.

[57] C. Ancin-Azpilicueta, A. Gonzalez-Marco, N. Jimenez-Moreno,

Crit. Rev. Food Sci. Nutr. 48 (2008) 257.

[58] J. Saurina, Characterization of wines through the compositional

profiles of biogenic amines and related compounds, focusing on

the description of toxicological and organoleptic features, in: P.

OByrne (Editor), Red Wine and Health, Nova Science Publishers,

Inc., NY, USA, 2009, pp. 630.

[59] I. Budic-Leto, G. Zdunic, J.G. Kljusuric, I. Pezo, I. Alpeza, T. Lovric,

J. Food Agric. Environ. 6 (2008) 138.

[60] E. Boselli, A. Giomo, M. Minardi, N.G. Frega, Eur. Food Res.

Technol. 227 (2008) 709.

[61] D.P. Makris, S. Kallithraka, A. Mamalos, Talanta 70 (2006)

1143.

[62] S. Kallithraka, A. Mamalos, D.P. Makris, J. Agric. Food Chem. 55

(2007) 3233.

[63] A. de Villiers, P. Majek, F. Lynen, A. Crouch, H. Lauer, P. Sandra,

Eur. Food Res. Technol. 221 (2005) 520.

[64] L.J. Kosir, B. Lapornik, S. Andrensek, A.G. Wondra, U. Vrhovsek,

J. Kidric, Anal. Chim. Acta 513 (2004) 277.

[65] M. Palma, C.G. Barroso, J.A. Perez-Bustamante, Analyst (Cam-

bridge, UK) 125 (2000) 1151.

[66] B. Berente, D.D.L. Garca, M. Reichenbacher, K. Danzer, J.

Chromatogr., A 871 (2000) 95.

[67] R. Romero, M. Sanchez-Vinas, D. Gazquez, M.G. Bagur, J. Agric.

Food Chem. 50 (2002) 4713.

[68] J. Kiss, A. Sass-Kiss, J. Agric. Food Chem. 53 (2005) 10042.

[69] A. Sass-Kiss, J. Kiss, B. Havadi, N. Adanyi, Food Chem. 110

(2008) 742.

[70] K. Heberger, E. Csomos, L. Simon-Sarkadi, J. Agric. Food Chem.

51 (2003) 8055.

[71] R. Castro, R. Natera, E. Duran, Garca-Barroso, Eur. Food Res.

Technol. 228 (2008) 1.

[72] B.T. Weldegergis, A.M. Crouch, J. Agric. Food Chem. 56 (2008)

10225.

[73] A. Tredoux, A. de Villiers, P. Majek, F. Lynen, A. Crouch, P.

Sandra, J. Agric. Food Chem. 56 (2008) 4286.

[74] J.M. Jurado, O. Ballesteros, A. Alcazar, F. Pablos, M.J. Martn, J.L.

Vlchez, A. Navalon, Anal. Bioanal. C hem. 390 (2008) 961.

[75] S. Cabredo-Pinillos, T. Cedron-Fernandez, C. Saenz-Barrio, Eur.

Food Res. Technol. 226 (2008) 1317.

[76] R. Marquez, R. Castro, R. Natera, C. Garca-Barroso, Eur. Food

Res. Technol. 226 (2008) 1479.

[77] D. Kruzlicova, J. Mocak, J. Hrivnak, J. Food Nutr. Res. 47 (2008)

37.

[78] E. Koussissi, A. Paterson, J.R. Piggott, Eur. Food Res. Technol. 225

(2007) 749.

[79] R.F. Alves, A.M.D. Nascimento, J.M.F. Nogueira, Anal. Chim. Acta

546 (2005) 11.

[80] N. Garca-Villar, J. Saurina, S. Hernandez-Cassou, Electrophoresis

27 (2006) 474.

[81] M. Scampicchio, S. Mannino, J. Zima, J. Wang, Electroanalysis

(NY) 17 (2005) 1215.

[82] E. Csomos, K. Heberger, L. Simon-Sarkadi, J. Agric. Food Chem.

50 (2002) 3768.

[83] L. Louw, K. Roux, A. Tredoux, O. Tomic, T. Naes, H.H.

Nieuwoudt, P. van Rensburg, J. Agric. Food Chem. 57 (2009)

2623.

[84] D. Ballabio, A. Mauri, R. Todeschini, S. Buratti, Anal. Chim. Acta

570 (2006) 249.

[85] F. Marini, R. Bucci, A.L. Magri, A.D. Magri, Chemom. Intell. Lab.

Syst. 84 (2006) 164.

[86] S. Perez-Magarino, M. Ortega-Heras, M.L. Gonzalez-San Jose, Z.

Boger, Talanta 62 (2004) 983.

[87] S. Kallithraka, I.S. Arvanitoyannis, P. Kefalas, A. El-Zajouli, E.

Soufleros, E. Psarra, Food Chem. 73 (2001) 501.

[88] D. Picque, T. Cattenoz, G. Corrieu, J. Int. Sci. Vigne Vin 35 (2001)

165.

[89] I.J. Kosir, M. Kocjancic, N. Ogrinc, J. Kidric, Anal. Chim. Acta 429

(2001) 195.

[90] G. Gonzalez, E.M. Pena-Mendez, Eur. Food Res. Technol. 212

(2000) 100.

[91] S. Rebolo, R.M. Pena, M.J. Latorre, S. Garca, A.M. Botana, C.

Herrero, Anal. Chim. Acta 417 (2000) 211.

[92] M.A. Brescia, I.J. Kosir, V. Caldarola, J. Kidric, A. Sacco, J. Agric.

Food Chem. 51 (2003) 21.

[93] M.E. Melendez, M.S. Sanchez, M. Inguez, L.A. Sarabia, M.C. Ortiz,

Anal. Chim. Acta 446 (2001) 159.



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