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Sensory-activecompoundsinfluencingwineexperts'andconsumers'perceptionofredwineintrinsicquality

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Sensory-active compounds influencing wine experts' and consumers'perception of red wine intrinsic quality

María-Pilar S�aenz-Navajas a, b, *, Jos�e-Miguel Avizcuri c, Jordi Ballester a, d,Purificaci�on Fern�andez-Zurbano c, Vicente Ferreira b, Dominique Peyron a, c,Dominique Valentin a, e

a Centre des Sciences du Goût et de l'Alimentation, UMR6265 CNRS e INRA-UB, 9E Boulevard Jeanne d'Arc, 21000 Dijon, Franceb Laboratory for Aroma Analysis and Enology (LAAE), Arag�on Institute of Engineering Research (I3A), Department of Analytical Chemistry,Faculty of Sciences, Universidad de Zaragoza, 50009 Zaragoza, Spainc Department of Chemistry, Universidad de La Rioja, Instituto de Ciencias de la Vid y el Vino, ICVV (UR-CSIC-GR), Madre de Dios 51, E-26006 Logro~no,La Rioja, Spaind IUVV Jules Guyot, Universit�e de Bourgogne, 1 rue Claude Ladrey, 21078 Dijon, Francee AGROSUP, Universit�e de Bourgogne, 1 Esplanade Erasme, 21000 Dijon, France

a r t i c l e i n f o

Article history:Received 24 January 2014Received in revised form2 September 2014Accepted 4 September 2014Available online xxx

Keywords:QualityVolatileNon-volatileSensory activityWine

* Corresponding author. Laboratory for Aroma ADepartment of Analytical Chemistry, Universidad de Z50009 Zaragoza, Spain.

E-mail addresses: maria@saenz.info, maria@saenz

http://dx.doi.org/10.1016/j.lwt.2014.09.0260023-6438/© 2014 Elsevier Ltd. All rights reserved.

Please cite this article in press as: S�aenz-Navof red wine intrinsic quality, LWT - Food Scie

a b s t r a c t

There is a lack of studies focusing on the chemical compounds involved in quality perception. Thepresent work combines both sensory and chemical approaches with the final goal of evaluating thesensory-active compounds influencing wine experts' and consumers' perception of red wine quality.

Perceived quality was categorised by 108 consumers and 119 experts according to four levels goingfrom very low to very high quality. In parallel, samples were described by a descriptive trained panel andvolatile and non-volatile chemicals with known sensory activity were quantified.

Wines with higher concentrations of eugenol, E� and Z-whiskylactones and 4-ethylphenol (discussedin terms of matrix effect) are perceived higher in quality by consumers, while fusel alcohols andastringent-related compounds such as PAs, cis-aconitic acid, certain flavonols and hydroxycinnamic acidderivatives are linked to lower quality samples. In contrast, experts perceived wines with lower levels ofwhiskylactones and volatile phenols while higher levels of norisoprenoids to be higher in quality.

These results increase the understanding of wine quality perception and can give the wine industryknowledge of the main sensory-active compounds driving quality for different wine consumers.

© 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Wine is a complex matrix comprising a wide range of volatileand non-volatile components able to generate sensory differencesin the overall wine flavour. Flavour scientists have tried to disclosethe various factors generating these sensory differences fromseveral fields of knowledge. In general, the main three approachesare psychophysical, chemical or sensory directed by studying bothsimple andmore complex mixtures of wine components or directlycomplex wine samples. Psychophysicists are centred on the studyof the sensory properties (quantitative and qualitative

nalysis and Enology (LAAE),aragoza, C/Pedro Cerbuna 12,

.es (M.-P. S�aenz-Navajas).

ajas, M.-P., et al., Sensory-actnce and Technology (2014), h

characteristics) of relatively simple mixtures of both aroma com-pounds (Atanasova et al., 2005; Ferreira, 2012) and tastants(Breslin, 2001) containing sensory-active molecules at supra-, peri-and sub-threshold concentration levels. Their final goal is to pro-vide models and theories that explain complex mixtures, such aswine. This information is valuable and constitutes a good base onwhich sound rules and concepts about the behaviour of sensory-active compounds in mixtures can be derived. However, to date,these psychophysical theories have had very little impact on wineflavour scientists.

Wine chemists tackle the understanding of wine flavour byanalysing both volatile and non-volatile compounds with sensoryactivity present in samples. Therefore, a great deal of sophisticatedchemical preconcentration, separation and detection techniqueshave been developed allowing analysis of compounds at concen-trations ranges from grams to nanograms per litre. Gas chroma-tography (GC) for volatile and liquid chromatography (LC) for non-

ive compounds influencing wine experts' and consumers' perceptionttp://dx.doi.org/10.1016/j.lwt.2014.09.026

M.-P. S�aenz-Navajas et al. / LWT - Food Science and Technology xxx (2014) 1e122

volatile compounds coupled to different detection systems such asmass spectrometry have permitted the identification and quanti-fication of an important number of wine components and further,the generation of fingerprints patterns based on chemicalcomposition. To provide a first approach of the sensory activity ofthe different molecules, a simplified form of the Stevens' law(Stevens, 1957) is usually applied to the quantitative data. This lawrelates the magnitude of a stimulus with the perceived intensity.The magnitude of the stimulus is usually estimated as the odouractivity value (Rothe & Thomas, 1963) for volatiles (OAV: ratiobetween the concentration of a compound and its sensorythreshold) or as the dose over taste-DoT- (Scharbert & Hofmann,2005) for non-volatile molecules. The perceived intensity hasdemonstrated to have an exponential relationship with themagnitude of the stimulus. As the exponent of the exponentialrelation is unknown for most compounds, a common approach isto assign to it a global value of one to be able to have relativelygeneral conclusions. Values of OAV or DoT higher than the unitsuggest that these molecules can be involved in the sensoryproperties of wines. Notwithstanding, the interaction of verysimilar odorants (in terms of aroma and structural terms) presentin wines at concentrations even below their sensory threshold canin certain cases exert a concerted action (Loscos, Hern�andez-Orte,Cacho, & Ferreira, 2007). This suggests that in some cases thearoma attributes cannot be associated to a single compound but toa mixture of odorants, which are called vectors. Interestingly, thiseffect could not be proved with non-volatile molecules involved intaste or astringency of wines (S�aenz-Navajas, Avizcuri, Ferreira, &Fern�andez-Zurbano, 2012). Besides, in complex mixtures such aswine, the final sensory properties are also the result of bothmasking and cooperative effects between sensory-active mole-cules (Lytra, Tempere, de Revel, & Barbe, 2012; Rigou, Triay, &Razungles, 2014), which are usually considered by calculatingPLS regression models. Notwithstanding, given the complexity ofwine flavour derived from sensory and chemical interactionsamong sensory-active compounds, the real sensory role ofchemicals in wine cannot be confirmed unless reconstitution ex-periments are performed and their real sensory impact is provenby sensory analysis.

With regard to sensory scientists, they have developed andapplied different sensory strategies for disclosing the sensoryprofile of the product as well as consumers' perception of differentwine sample sets. On the one hand, conventional descriptiveanalysis is the most extended method used to establish bothquantitative and qualitative differences betweenwine samples andto obtain their sensory profiles. Other alternative methods such asfree profiling, free sorting task or projective mapping among othershave an increasing acceptance (Valentin, Chollet, Lelievre, & Abdi,2012). On the other hand, the study of hedonic consumers'perception of wines (based on flavour perception) has been mainlybased on the explanation of differences among consumers attrib-uted to different cultures (Williamson, Robichaud, & Francis, 2012)or levels of expertise (Torri et al., 2013) among others. The combi-nation of sensory profiling and hedonic ratings has permitted toestablish the main sensory drivers of either consumers' preference,acceptability (Lattey, Bramley, & Francis, 2010) or quality percep-tion (Varela & Gambaro, 2006) of wines.

The combination of flavour chemistry with sensory analysistechniques has allowed associating wine sensory properties tospecific chemical compounds (Robinson et al., 2011). Thus, a rela-tively important number of theoretical models predicting aromaproperties have been constructed. However, only few of them havebeen confirmed by real addition experiments (San Juan, Ferreira,Cacho, & Escudero, 2011). Similarly, there is a lack of studiesfocusing on the main wine chemicals driving consumers'

Please cite this article in press as: S�aenz-Navajas, M.-P., et al., Sensory-actof red wine intrinsic quality, LWT - Food Science and Technology (2014), h

preference or quality perception by combining hedonic ratings ofconsumers and chemical data.

Understanding the mechanisms underlying wine qualityperception is important as they are involved in the decision-makingprocess of consumers when purchasing a bottle of wine (Marin,Jorgensen, Kennedy, & Ferrier, 2007). Quality is generally definedas the judgment about a product's overall excellence or superiority.This is a concept difficult to define since it is a complex terminfluenced by product-related (such as flavour when related tointrinsic quality) and consumer-related (such as level of involve-ment, country of origin, expectation …) factors (Charters &Pettigrew, 2007). In particular, the level of expertise of consumershas been demonstrated to influence wine perception (Lattey et al.,2010). Experts' judgments are usually based on technical wine-making processes while consumers' assessments are based on theirindividual and subjective experience. This gives rise to amisalignment between the quality concept of wine professionalsand regular consumers.

The present work was aimed at firstly establishing the maincorrelations between the chemical composition and the sensoryproperties of a set of wines and further identifying the mainchemical drivers of the intrinsic quality evaluated by assessors withdifferent levels of expertise (wine professionals or experts vs reg-ular consumers). A systematic sensory description and chemicalanalysis of volatile and non-volatile molecules with known sensoryactivity has been carried out. In parallel, the intrinsic quality eval-uation of the same set of wines was performed by experts andregular consumers.

Different researches (Lattey et al., 2010; Varela & Gambaro,2006) correlating intrinsic quality perception and sensory attri-butes showed positive correlations between fruity (berry or driedfruit depending on the type of wine) and woody aromas as well asastringency and the quality scores given by experts (or highlyinvolved wine consumers). Thus, it was firstly hypothesised thatvolatile compounds such as ethyl esters related to fruity aroma andcertain volatile phenols linked to woody nuances, as well as non-volatile polyphenol compounds with astringent properties wouldbe positive drivers of intrinsic quality of experts. Secondly, giventhe differences in the construction of wine concepts of wine expertsand regular consumers (Parr, Mouret, Blackmore, Pelquest-Hunt, &Urdapilleta, 2011), it was hypothesised that the sensory andchemical drivers of quality would also differ between them.

2. Materials and methods

2.1. Wines

Twelve commercial red wines from DOCa Rioja in Spain (6samples) and AOC Cotes Du Rhone in France (6 samples) werestudied. Their prices ranged between 2V and 12V in the Spanishand French markets. These wines were selected in consultationwith wine experts in both production areas with the aim of havingwines representative of each region and with important differ-ences in their intrinsic properties able to generate differentintrinsic quality perception. Detailed information concerning se-lection strategy is provided elsewhere (S�aenz-Navajas, Ballester,Pecher, Peyron, & Valentin, 2013). The list of samples, includingsample information and basic compositional data is shown inTable 1.

2.2. Chemical analysis

2.2.1. Conventional oenological parameter determinationEthanol content, pH, reducing sugars, titratable (total) and vol-

atile acidities were determined by Infrared Spectrometry with

ive compounds influencing wine experts' and consumers' perceptionttp://dx.doi.org/10.1016/j.lwt.2014.09.026

Table 1Wines used for the study and their denomination of origin, vintage, oak ageing time, grape variety and conventional oenological parameters.

Code Denominationof Origina

Vintage Monthsinoakbarrels

Grape variety Alcohol% (v/v)

pH Volatileacidity(g L�1)b

Totalacidity(g L�1)c

Reducingsugars(g L�1)

Malic acid(g L�1)

Lactic acid(g L�1)

TPId

(a.u.)

CdR-081 AOC CdR 2010 0 85% Grenache,15% Syrah

14.2 ± 0.01 3.57 ± 0.01 0.28 ± 0.01 5.17 ± 0.01 2.16 ± 0.20 0.16 ± 0.01 1.17 ± 0.04 58.9 ± 0.6

CdR-102 AOC CdRValr�eas

2010 0 30% Grenache,70% Syrah.

14.6 ± 0.01 3.49 ± 0.01 0.49 ± 0.02 6.00 ± 0.00 2.34 ± 0.09 0.17 ± 0.01 1.40 ± 0.04 61.1 ± 0.1

CdR-333 AOC CdR 2007 * 50% Syrah, 40%Grenache,10% Mourvedre

14.9 ± 0.01 3.62 ± 0.00 0.56 ± 0.01 4.95 ± 0.01 3.47 ± 0.04 0.17 ± 0.01 1.21 ± 0.06 58.8 ± 0.2

CdR-888 AOC CdRPlande Dieu

2009 0 Grenache, Syrah,Carignan

14.4 ± 0.01 3.52 ± 0.01 0.54 ± 0.01 5.31 ± 0.00 3.15 ± 0.11 0.21 ± 0.02 0.88 ± 0.06 59.81.1

CdR-903 AOC CdR 2010 0 Grenache noir,Syrah, Carignan

14.2 ± 0.01 3.50 ± 0.01 0.37 ± 0.01 5.53 ± 0.00 2.30 ± 0.04 0.16 ± 0.01 1.04 ± 0.06 57.0 ± 0.6

CdR-936 AOC CdRPlande Dieu

2009 0 Grenache, Syrah,Carignan

15.3 ± 0.01 3.61 ± 0.01 0.34 ± 0.01 5.01 ± 0.00 2.72 ± 0.07 0.27 ± 0.06 0.87 ± 0.08 70.3 ± 1.1

RJ-005 DOCa RJReserva

2005 26 Tempranillo,Graciano, Mazuelo

14.0 ± 0.01 3.55 ± 0.01 0.50 ± 0.01 5.36 ± 0.01 1.83 ± 0.01 0.14 ± 0.01 1.66 ± 0.06 51.4 ± 0.1

RJ-058 DOCa RJCosecha

2009 4 90% Tempranillo10% Graciano

14.1 ± 0.00 3.60 ± 0.00 0.62 ± 0.02 5.92 ± 0.01 2.86 ± 0.04 0.40 ± 0.00 1.78 ± 0.08 56.0 ± 0.5

RJ-381 DOCa RJCosecha

2010 4 100% Tempranillo 13.8 ± 0.01 3.68 ± 0.01 0.53 ± 0.01 5.33 ± 0.01 1.84 ± 0.13 0.13 ± 0.01 1.67 ± 0.06 54.9 ± 0.1

RJ-690 DOCa RJReserva

2005 ** 100% Tempranillo 13.9 ± 0.00 3.48 ± 0.00 0.53 ± 0.01 5.82 ± 0.00 2.31 ± 0.09 0.18 ± 0.02 1.46 ± 0.07 53.3 ± 0.5

RJ-774 DOCa RJReserva

2005 25 100% Tempranillo 14.2 ± 0.01 3.51 ± 0.00 0.63 ± 0.01 5.82 ± 0.03 2.15 ± 0.04 0.14 ± 0.00 1.77 ± 0.07 52.0 ± 0.6

RJ-917 DOCa RJCrianza

2007 12 85% Tempranillo,15% Grenache

14.2 ± 0.00 3.43 ± 0.00 0.48 ± 0.01 5.82 ± 0.01 2.52 ± 0.08 0.19 ± 0.01 1.25 ± 0.04 52.8 ± 0.7

*Aged in oak barrels but information about the time is not available.**Aged in oak barrels for at least 12 months, but the specific time is not available.

a Reserva: the wine has been aged for at least one year in oak barrels and two years in the bottle before commercial release; Crianza: at least one year in oak barrels and oneyear in the bottle before commercial release; Cosecha: less than one year in oak barrels.

b Expressed as g L�1 of acetic acid.c Expressed as g L�1 of tartaric acid.d Total Polyphenol Index expressed as absorbance units (a.u.).

M.-P. S�aenz-Navajas et al. / LWT - Food Science and Technology xxx (2014) 1e12 3

Fourier Transformation (IRFT) with a WineScan™ FT 120 (FOSS®),which was calibrated with wine samples analysed in accordancewith official OIV practices. Malic and lactic acids were determinedby enzymatic methods using a LISA 200 Wine Analyzer System.Total Polyphenol Index (TPI) was estimated as absorbance at280 nm measured in 1-cm cuvettes multiplied by 100.

2.2.2. Quantification of individual non-volatile compounds byUPLC-MS

UPLC analyses were performed using a Waters Acquity UltraPerformance LC system (Milford, MA, USA) by direct injection ofwine samples as described in Appendix A.

2.2.3. Total polymeric proanthocyanidin (PA) analysisWine samples were firstly fractionated by preparative chroma-

tography: Gel Permeation Chromatography (GPC) as describedelsewhere (S�aenz-Navajas, Fern�andez-Zurbano, Tao, Dizy, &Ferreira, 2010) and total polymeric PAs were quantified in frac-tion 2 by the vanillin assay according to the method describedelsewhere (Sun, Leandro, Ricardo-da-Silva, & Spranger, 1998). An-alyses were performed in duplicate.

2.2.4. Protein-precipitable PA analysisThe concentration of protein-precipitable PAs was estimated

using ovalbumin as the precipitation agent and tannic acid solu-tions as standards in accordance with a previously describedmethod (Llaudy et al., 2004). Analyses were performed induplicate.

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2.2.5. Volatile composition analysisThe quantification of major and minor compounds was carried

out using the methods proposed and validated by Ortega, L�opez,Cacho, and Ferreira (2001) and by L�opez, Aznar, Cacho, andFerreira (2002) with the modifications introduced by Loscos et al.(2007), respectively.

2.3. Sensory analysis

Two sensory tasks were performed in parallel. The first taskaimed at describing the sensory properties of the 12 wines and wascarried out by trained panellists. The second task aimed at evalu-ating the perceived quality of the 12 wines and was carried out by, apanel of regular consumers and a panel of wine experts. None of theparticipants were informed about the nature of the samples.

2.3.1. Wine description by trained panellistsA total of 58 participants (30 women and 28 men from 20 to 56

years; average ¼ 32; SD ¼ 10) were recruited on the basis of theirinterest and their availability during the 9 months betweenSeptember 2011 and June 2012. Panellists attended 21 descriptivesensory training sessions during which they worked in subgroupsof five to seven people. They were not paid for their participation.The frequency of citation method was used for orthonasal aromadescription as described elsewhere (S�aenz-Navajas et al., 2013).Sweetness, acidity, bitterness and astringency were scored on six-point scales (0 ¼ ‘absence’, 1 ¼ ‘very low’ and 5 ¼ ‘very high’),global intensity (1 ¼ ‘very low’ and 5 ¼ ‘very high’) and persistence(1 ¼ ‘very short’ and 5 ¼ ‘very long’) on five-point scales as the

ive compounds influencing wine experts' and consumers' perceptionttp://dx.doi.org/10.1016/j.lwt.2014.09.026

Table 2Concentration ranges and median values of volatiles found in the set of the 12 wines (all expressed in micrograms per litre). Differentiation ability calculated as the quotientbetween maximum odour activity value (OAV) and minimum OAV and correlation coefficients (r) of linear regression between the OAV of each compound and qualityevaluated by experts and consumers. Compounds found in at least one wine at concentrations above their sensory threshold are marked in bold.

Compounds Odour thresholda Concentration ranges Median OAVmax/OAVminb r (expert Q)c r (consumer Q)d

Acetates2-methylpropyl acetate 1600 (1) 20.4e106 68.7 0.33 0.24 0.22Butyl acetate 1800 (2) <0.54e8.01 1.86 0.02 0.27 0.17Isoamyl acetate 30 (3) 200e600 260 3.02 0.18 ¡0.65**

AcidsAcetic acid 300,000 (1) 206040e437240 353,430 2.12 ¡0.21 0.50*2-methylpropanoic acid 50 (1) 2200e3870 3040 1.76 ¡0.35 0.22butyric acid 173 (4) 990e1860 1380 1.88 ¡0.08 0.393-methylbutyric acid 33 (4) 1430e2430 1830 1.70 ¡0.19 ¡0.37Hexanoic acid 420 (4) 1200e2880 1890 2.39 ¡0.16 ¡0.03Octanoic acid 500 (4) 1220e2680 1870 2.19 ¡0.27 ¡0.33Decanoic acid 1000 (4) 180e1290 510 6.46 ¡0.29 ¡0.57**

Alcohols2-methylpropanol 40,000 (3) 42320e79850 58203 1.89 0.01 ¡0.63**1-butanol 150,000 (2) 1020e1510 1320 0.05 0.19 0.44Isoamyl alcohol 30,000 (3) 250670e376300 282570 1.50 ¡0.29 ¡0.63**1-hexanol 8000 (3) 910e2240 1840 1.40 0.05 0.41Z-3-hexenol 400 (3) 60e340 160 4.21 �0.03 0.20Methionol 1000 (4) 1360e3070 1710 2.25 0.22 ¡0.07Benzyl alcohol 200,000 (5) <20e53 20 0.00 0.21 �0.53b-phenylethanol 14,000 (4) 33040e58430 46760 1.77 ¡0.09 ¡0.48

Carbonylic compoundsBenzaldehyde 2000 (6) <0.20e59.9 2.93 0.15 �0.23 0.12b-damascenone 0.05 (3) 0.29e2.59 1.05 8.87 0.42 ¡0.14b-ionone 0.09 (2) <0.09e0.44 0.20 4.99 0.06 0.24Acetaldehyde 500 (3) 2930e17130 9700 5.84 0.01 0.60**Acetoin 150,000 (4) 4408e40110 20020 1.34 0.13 0.32

EstersEthyl 2-methylpropanoate 15 (4) 97.9e410 266 4.20 0.22 0.23Ethyl 2-methylbutyrate 18 (4) 3.23e48.7 20.1 15.04 0.05 0.55*Ethyl 3-methylbutyrate 3 (4) 5.22e67.4 26.3 12.92 0.04 0.53*Ethyl furoate 160,000 (4) 1.21e11.89 6.42 3.96 0.19 0.30Ethyl cinnamate 1.1 (4) 0.28e2.53 0.72 0.84 0.32 ¡0.17Ethyl acetate 12300 (7) 51900e122770 94170 2.37 ¡0.22 0.69**Ethyl propanoate 5500 (8) 90e470 190 0.43 0.08 0.46Ethyl butyrate 125 (8) 160e280 200 1.76 ¡0.03 0.15Ethyl hexanoate 62 (8) 210e530 310 2.56 ¡0.10 ¡0.04Ethyl lactate 154,000 (2) 131930e324040 189390 2.46 ¡0.20 0.62Ethyl octanoate 580 (2) 200e400 270 1.96 �0.05 �0.32Ethyl decanoate 200 (4) 170e470 260 2.83 ¡0.05 0.06Diethyl succinate 200,000 (2) 10800e22050 13260 0.55 �0.40 0.71**

Volatile phenolsGuaiacol 9.5 (4) 2.48e17.2 6.88 6.94 ¡0.09 0.48o-cresol 31 (2) 0.73e4.29 1.58 0.69 0.08 0.304-ethylguaiacol 33 (4) 0.16e222 42.76 33.74 ¡0.19 0.31m-cresol 68 (9) 0.39e2.93 1.74 0.22 0.36 0.324-propylguaiacol 10 (10) <0.048e8.37 0.41 4.19 0.16 0.54*Eugenol 6 (4) 1.42e71.6 5.56 59.6 0.13 0.66**4-ethylphenol 35 (8) 0.57e3940 601.3 562 ¡0.22 0.52*4-vinylguaiacol 40 (3) 0.45e58.2 6.74 7.27 ¡0.02 ¡0.212,6-dimethoxyphenol 570 (10) 10.3e58.4 28.78 0.51 0.06 0.62**4-vinylphenol 180 (11) 1.60e6.72 3.35 0.19 �0.11 0.164-allyl-2.6-dimethoxyphenol 1200 (12) 1.74e49.2 9.53 0.20 0.08 0.71**Vanillin 995 (5) 4.43e378 30.62 1.90 0.25 0.44Methyl vanillate 990 (10) 2.51e67.2 23.72 0.34 0.05 �0.51*Ethyl vanillate 3000 (10) 171e897 385.58 1.50 0.05 0.56*Acetovanillone 1000 (7) 62.9e323 172.11 1.62 0.24 0.19Syringaldehyde 50,000 (12) <0.385e1569 37.72 0.16 0.35 0.16

LactonesE-whiskylactone 790 (2) <0.021e166 13.72 1.06 0.29 0.63**Z-whiskylactone 67 (2) <0.130e732 64.21 54.64 0.13 0.67**g-nonalactone 25 (12) 4.35e24.1 13.52 4.81 0.25 0.21g-decalactone 0.7 (12) 0.61e3.50 1.85 5.73 0.31 0.34d-decalactone 386 (13) <0.014e78.2 7.16 1.01 �0.13 0.43g-butyrolactone 35,000 (5) 12640e23660 17640 1.87 �0.12 �0.03

TerpenolsLinalool 25 (4) 1.04e17.2 5.73 3.44 0.32 �0.42a-terpineol 250 (4) 1.61e17.9 7.24 0.36 0.19 �0.04b-citronellol 100 (2) 1.52e16.4 5.29 0.82 0.27 �0.34geraniol 20 (5) <0.010e7.70 1.90 1.93 0.34 �0.59**

Odour thresholds determined by triangular tests and in different matrices (In refs 1, 5, 7e10, and 13 the matrix was a 10% water/ethanol solution at pH 3.2; in ref 2 thresholdswere calculated in wine, in ref 3 was 10% in ethanol, in ref 4 was 11% in ethanol containing 7 g L�1 glycerol and 5 g L�1 tartaric acid, pH adjusted to 3.4 with 1 M NaOH. In ref 6

M.-P. S�aenz-Navajas et al. / LWT - Food Science and Technology xxx (2014) 1e124

Please cite this article in press as: S�aenz-Navajas, M.-P., et al., Sensory-active compounds influencing wine experts' and consumers' perceptionof red wine intrinsic quality, LWT - Food Science and Technology (2014), http://dx.doi.org/10.1016/j.lwt.2014.09.026

was 10% in ethanol adjusted to pH 3.5 with tartaric acid, in ref 11 the matrix was a synthetic wine containing 12% ethanol, 8 g L�1 glycerol and different salts. In ref 12 thematrix was water).

a Reference in which the odour threshold value has been calculated is given in brackets. 1: Ferreira, Ortin, Escudero, L�opez, and Cacho (2002), 2: Etievant (1991), 3: Guth(1997), 4: Ferreira, L�opez, and Cacho (2000), 5: Escudero et al. (2007), 6: Peinado, Moreno, Bueno, Moreno, and Mauricio (2004), 7: Escudero et al. (2004), 8: San Juan et al.(2011), 9: Ferreira et al. (2009), 10: L�opez et al. (2002), 11: Boidron, Chatonnet, and Pons (1988), 12: Van Gemert (2003), 13: Ferreira, Aznar, and L�opez (2001).

b For OAVminimum <0.2, this value is considered for calculating the quotient.c Correlation coefficient of linear regression between OAVs and quality evaluated by experts (significant correlations: *P < 0.1; **P < 0.05).d Correlation coefficient of linear regression between OAVs and quality evaluated by consumers (significant correlations: *P < 0.1; **P < 0.05).

M.-P. S�aenz-Navajas et al. / LWT - Food Science and Technology xxx (2014) 1e12 5

absence-point was meaningless. For the analysis of the 12 winestwo formal sessions were completed (12 samples þ 2 replicates:one replicate per session). Seven samples were presented in eachsession during which panellists started by smelling the first wineand describing its orthonasal aroma choosing a maximum of fivedescriptors from the list. Then, they had to taste the wine and ratethe six in-mouth properties (taste and mouthfeel) with the scalesdescribed above. Fifteen-millilitre wine samples were presented inISO-standardised dark wine glasses labelled with three-digitrandom codes and covered by plastic Petri dishes according to arandom arrangement. All wines were served 15 min before formaltasting and presented at room temperature (20e22 �C) in indi-vidual booths.

2.3.2. Quality evaluation of wines by consumers and expertsDetails on the recruitment of both regular consumers and ex-

perts can be seen in Appendix B. The experimental conditions wereidentical for both regular consumers and experts for evaluatingintrinsic quality. Fifteen minutes before formal tasting wines wereserved at room temperature in clear ISO glasses coded with threedigit numbers and covered with plastic Petri dishes. Intrinsicquality assessment of the 12 wine samples was carried out by fourpanels: Spanish experts (59 participants) and consumers (56 par-ticipants), French experts (60 participants) and consumers (52participants). Each participant evaluated exclusively the intrinsicquality of wines, using their own criteria in individual tastingbooths in one 20-min session. The 12 samples were presented ac-cording to a circular incomplete balanced experimental designfollowing a Latin square arrangement (presented in series of sixwine samples). Participants were asked to taste the wine samplesonce in the proposed order. Then, they had to sort the samplesaccording to their global intrinsic quality perception in fourdifferent quality categories (1 ¼ very low quality, 2 ¼ low quality,3 ¼ high quality and 4 ¼ very high quality). These four categorieswere easily interpretable by consumers and avoided centring biases(tendency to use the middle category). During this categorisationtask, participants were allowed to taste the wines as many times asthey wanted. Quality assessments were carried out in March 2012in Logro~no (Spain) at La Rioja University and in April 2012 inAvignon area (France) at Inter Rhone for consumers and experts. Allassessments were conducted by the same leader under the sameinstructions for the four panels.

2.4. Data analysis

2.4.1. Sensory descriptive data derived from the trained panelFrequencies of citation (FC) scores, derived from the orthonasal

aroma description carried out by the trained panel, were computedfrom the number of times an individual term was selected for agiven wine. To assess the individual performance of panellists, anaverage reproducibility index (Ri) was calculated for each of thepanellists from duplicate assessments of wines (S�aenz-Navajaset al., 2013). This parameter, ranges from 0 to 1 and the minimumRi required to keep a judge response was set at 0.20. Further, Cor-respondence Analysis (CA) was performed on the contingency tablecontaining the FC of terms most cited. Thus, only terms cited at

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least by 15% of panellists were arbitrary considered. The number offactors with an eigenvalue higher than the mean eigenvalue (Kaiserlaw) were considered significant in the CA space.

Scores derived from in-mouth description were submitted toone-way analysis of variance (ANOVA) on each descriptor (inwhichwine was the fixed factor and judge the random factor) for evalu-ating their discrimination abilities. Only “astringency” showed asignificant wine effect, which suggests that this attribute is the soleterm useful in characterising in-mouth differences among the 12wines according to the trained panel. Thus, only the scores obtainedfor this in-mouth attribute were considered for further discussion.

2.4.2. Sensory quality assessment by experts and regular consumersThe quality scores given by either consumers (responses of

Spanish and French consumers were processed together) or experts(responses of Spanish and French experts were processed together)were projected as illustrative quantitative variables in the CA map.Hierarchical Cluster Analysis (HCA) with the Ward criteria wasfinally applied. The attributes best defining the resulting wineclusters were identified by computing their probability of charac-terising a cluster. All analyses were carried out with SPAD software(version 5.5, CISIA-CESRESTA, Montreuil, France).

2.4.3. Chemical dataQuantitative data of volatile and non-volatile compounds of

Tables 2 and 4 were transformed into Odour Activity Values (OAV)and Dose of Taste (DoT), respectively, by dividing by their corre-sponding sensory thresholds (tabulated in Table 3 for aroma and inTable 4 for astringency thresholds). In the case of concentrationsunder detection and quantification limits, these values wereconsidered to calculate OAVs and DoTs. In order to rank compoundsin accordance to their discriminatory ability, the quotients betweenthe maximum and minimum OAV or DoT were worked out for eachcompound (in case of OAV or DoT minimum < 0.2, this value wasarbitrary used for avoiding quotients with no sense from a sensorypoint of view, especially when OAVmin or DoTmin are zero).

Correlation coefficients of linear regression (r) between the OAVor DoT of each compound and the quality scores given by bothexperts (Q experts) and consumers (Q consumers) (Tables 2 and 4)were calculated with the 12 wines using Excel.

Fourteen aroma vectors were built with similar odorants (inboth structure and odour properties). Therefore, the individual OAVfor each compound belonging to each vector was firstly calculatedand aroma vectors were built by adding their OAVs. The “nor-isoprenoid vector” was constructed with b-damascenone and b-ionone. Eight phenolic compounds (guaiacol, o-cresol, m-cresol, 4-vinylguaiacol, 2,6-dimethoxyphenol, 4-ethylguaiacol, 4-allyl-2,6-dimethoxyphenol, 4-propylguaiacol) were grouped to form the“volatile phenol vector”. The “whiskylactone vector” was con-structed with two lactones (E� and Z-whiskylactones). The “linearfatty acid vector” was formed by butyric, hexanoic, octanoic anddecanoic acids and the “branched fatty acid vector” by 3-methylbutyric and 2-methylpropanoic acids. The “acetate vector”was formed by isoamyl, isobutyl and ethyl acetates, the “fuselalcohol vector” by isobutanol and isoamyl alcohol, the “vegetalalcohol vector” by Z-3-hexenol and 1-hexanol. The “linear ester

ive compounds influencing wine experts' and consumers' perceptionttp://dx.doi.org/10.1016/j.lwt.2014.09.026

M.-P. S�aenz-Navajas et al. / LWT - Food Science and Technology xxx (2014) 1e126

vector” was composed by five linear ethyl esters: proponoate,butyrate, hexanoate, octanoate and decanoate ethyl esters and the“branched ester vector” by isobutyrate, 2-methylbutyrate, iso-valerate, furoate, cinnamate ethyl esters. The “terpenol vector” bygeraniol, b-citronellol, a-terpineol and linalool, the “vanilla vector”by vanilla, acetovanillone, ethyl vanillate and methyl vanillate, the“ethylphenol vector” by 4-ethylphenol and 4-ethylguaiacol and the“lactone vector” by g-nonalactone, g-decalactone and d-deca-lactone. Partial least-squares regression (PLSR) was used to eval-uate the relationship between the FC for the sensory aroma termsderived from the description carried out by the trained panel andthe volatile composition measured in the set of wines. Only a sig-nificant model was obtained for the cherry term. PLSR was per-formed with Unscrambler 9.7 (CAMO).

Principal component analysis (PCA) was carried out with the 14aroma vectors and four individual compounds (methionol, b-phe-nylethanol, acetaldehyde and eugenol) as active variables andquality scores of both experts and consumers as illustrative vari-ables with XLSTAT software (Version 2014.2.02).

3. Results and discussion

3.1. Intrinsic quality evaluation

The quality scores given by both panels of experts (Spanish andFrench experts) are significantly correlated (r ¼ 0.616; P < 0.05) asare the scores given by both panels of Spanish and French regularconsumers (r ¼ 0.590; P < 0.05). On the contrary, no significantcorrelation is observed between experts and regular consumerswithin or between countries. This fact suggests that the concept ofwine quality perception is a function of expertise as shown forcomplexity (Parr et al., 2011).

Given the significant correlation between the quality scoresprovided by participants with similar level of expertise in bothregions as well as for the sake of simplicity in the exposition ofresults, quality scores of experts (total of 119 participants) and ofregular consumers (total of 108 participants) from both countrieshave been grouped and processed together in the present work.

The quality scores of the 12 wines range from 1.69 to 3.18 forexperts and from 1.28 to 3.31 for regular consumers, being 1 and 4the minimum and maximum possible scores, respectively. For bothexperts and regular consumers the wine scored lowest in quality isCdR-102 (young wine of vintage 2010), while the highest in qualityis CdR-081 (young wine of vintage 2010) for experts and RJ-774(Reserva wine of vintage 2007) for regular consumers.

It is interesting to note that quality scores are not significantlycorrelated with wine prices, but a positive tendency is observed forconsumers' quality scores (r ¼ 0.55; P ¼ 0.065), on the contrary anegative tendency is observed for experts' quality scores(r ¼ �0.57; P ¼ 0.051).

3.2. Sensory properties and their correlation with qualityperception

The correspondence analysis plot derived from the orthonasalaroma characterisation carried out by the trained panel is shown inFig.1. HCA applied to the factorial coordinates of the 12wines in theCA space yielded three clusters. The first cluster is projected on thelower part of the plot and is formed by six wines (RJ-005, RJ-058,RJ-381, RJ-690, RJ-774, RJ-917), which is significantly charac-terised by the attributes dried apricot, fresh wood and leather. Thesecond cluster, plotted on the upper part of the plot is constitutedby five wines (CdR-081, CdR-903, CdR-936, CdR-888 and CdR-333),which is significantly described by terms such as red fruits (morespecifically: cherry) and alcohol. The third cluster, which comprises

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a single wine: CdR-102, is characterised by animal (perspiration, caturine), vegetal (vegetables, blackcurrant bud) and sulphurattributes.

The projection of wine quality scores (evaluated by regularconsumers and experts) as illustrative variables on the CA plotallows us to establish links between quality perception and wines.Results show that both consumers and experts agree in findingwine CdR-102 of low quality. Regular consumers score wines ofCluster 1 (dried apricot, fresh wood and leather) mainly high inquality, while wines belonging to Cluster 2 (cherry and alcohol)are perceived as lower quality exemplars. Just the opposite isfound for the quality scores given by experts. This is in accordancewith previous studies that show wine age (more specifically agedin oak barrels) as an important intrinsic (Jover, Montes, & Fuentes,2004) and extrinsic (Mtimet & Albisu, 2006; S�aenz-Navajas et al.,2014) cue driving red wine quality perception for regular con-sumers (not professionally linked to wine industry). Thus, olderwines (especially Reserva wines, which are longer time aged inbarrels) are related to excellence and quality. On the contrary, theconstruct of quality for wine experts seems to be especiallyweighted toward extrinsic technical factors of wine production.Thus, the presence of a leather character in wines of Cluster 1,which is perceived by experts as a default derived from inap-propriate wine production, would have led them to score lower inquality wines aged in barrels (Cluster 1) and higher in qualityyounger wines (Cluster 2).

Concerning the in-mouth sensation of astringency, it does notpresent a significant correlation with experts' quality perception,but it is negative (r ¼�0.43; P ¼ 0.091) and close to significance forconsumers' quality scores.

3.3. Aroma-active volatile compounds explaining wine aromadifferences and quality perception

The study of the volatile composition of the 12 red wines hasprovided quantitative data for 62 compounds belonging to severalimportant families of wine aroma (Table 2). Two criteria areapplied to evaluate sensory differences among wines derived fromtheir chemical composition: (i) discrimination ability of com-pounds and (ii) sensory activity. According to the first criterion,volatile compounds with a potential ability to induce sensorydifferences among wine samples are those with higher OAVmax/OAVmin rates. On the basis of this quotient, the most discriminantodorants are a group of woody-like aromas (eugenol and Z-whiskylactone) as well as 4-ethylguaiacol and 4-ethylphenol(leather-like odorants), which all show OAVmax/OAVmin >30(Table 2). The highest discrimination ability of these compoundscould explain the sensory aroma differences found on the seconddimension between the wines of Cluster 1, which are mainlydescribed with woody and leather character (plotted on the lowerquadrant of Fig. 1 and acquiring negative scores on PC2) and winesof Cluster 2, described with red fruit and alcohol attributes (pos-itive scores on PC2).

According to the sensory activity criterion, compounds presentat concentrations lower than their sensory threshold are not ex-pected to have an important sensory impact on the sensory prop-erties of wines, except for the cases in which there are severalcompounds sharing a principal or secondary sensory descriptorwhich may act additively together (see for instance Loscos et al.,2007). In Table 2, the 31 compounds present at concentrationsabove their sensory threshold are marked in bold. In addition, mostcompounds sharing chemical structure and aroma properties aregrouped to form “vectors” even if their individual concentrationsare lower than their sensory thresholds. These groups of com-pounds are thought to have the potential ability of exerting a

ive compounds influencing wine experts' and consumers' perceptionttp://dx.doi.org/10.1016/j.lwt.2014.09.026

Table 3Odour thresholds and mean concentrations (expressed in micrograms per litre) of volatile compounds with significant differences. Spanish wines of Cluster 1 (6 samples),French wines of Cluster 2 (5 samples) and wine CdR-102 (1 sample). Significant differences (P < 0.01***; P < 0.05**; P < 0.1*) are calculated with ANOVA tests (for a compound,clusters with different letters are significantly different according to NewmaneKeuls post-hoc test).

Compounds Odor thresholda Spanish wines(Cluster 1)

French wines(Cluster 2)

CdR-102(Cluster 3)

P

AcetatesIsoamyl acetate 30 230a 350b 517c **

AcidsAcetic acid 300,000 403,550c 297410a 338,446b **2-methylpropanoic acid 50 3080a 2780a 3870b **Butyric acid 173 1550c 1200a 1422b **3-methylbutyric acid 33 1720a 1880b 2431c **Hexanoic acid 420 2200b 1540a 2152b ***Octanoic acid 500 1940b 1780b 2020b **Decanoic acid 1000 560a 590a 790b ***

Alcohols2-methylpropanol 40,000 53,010a 61,500b 79,854c *Isoamyl alcohol 30,000 271,310a 288,730b 376,299c **1-hexanol 8000 2070c 1390a 1705b ***Z-3-hexenol 400 240b 110a 165c **

b-phenylethanol 14,000 41270a 49,170b 47,152b **AldehydeAcetaldehyde 500 12.53c 8.64b 2.93a ***

EstersEthyl acetate 12300 109,220c 83,400b 78,179a ***Ethyl butyrate 125 240c 180a 201b **Ethyl hexanoate 62 380c 260a 307b **Ethyl lactate 154,000 243,140c 170,410a 195,896b ***

Volatile phenols4-propylguaiacol 10 3.18c 0.35a 0.05a *Eugenol 6 37.43c 5.13b <0.074a **4-ethylphenol 35 1502c 605b 124.6a *4-vinylguaiacol 40 3.61a 20.5c 13.3b **4-allyl-2,6-dimethoxyphenol 1200 28.7c 7.34b 3.33a **Vanillin 995 186.6b 17.5a 7.79a **Methyl vanillate 990 12.8a 37.1b 45.6c ***

LactonesE-whiskylactone 790 98.8c 10.7b <0.021a ***Z-whiskylactone 67 411c 53.2b 0.13a **d-decalactone 386 33.8c 3.26a 21.8b **d-butyrolactone 35,000 16,510a 19770c 18,650b **

TerpenolsLinalool 25 4.70a 8.84c 6.25b **b-citronellol 20 4.56a 8.15c 6.24b **

a Reference in which the odour threshold value has been calculated is given in Table 2.

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cooperative effect when generating certain aromas. Fourteenaroma vectors are constructed: linear fatty acids, branched fattyacids, norisoprenoids, linear ethyl esters, branched ethyl esters,acetates, vegetal alcohols, fusel alcohols, terpenols, vanillas, ethyl-phenols, volatile phenols, whiskylactones and lactones (see dataanalysis section).

The sensory properties dominating wines in the first cluster(dried apricot, fresh wood and fresh wood) can be explained bytheir highest concentrations of oak-related compounds such as:eugenol, 4-propylguaiacol, 4-allyl-2,6-dimethoxyphenol, vanillin,E-whiskylactone and Z-whiskylactone (Table 3), leading to thehighest values for both volatile phenol and whiskylactone vectors.These results are further confirmed by significant positive corre-lations between the “fresh wood” scores given by the trained paneland chemical compounds such as vanillin (r ¼ 0.77; P < 0.05), thewhiskylactone vector (r ¼ 0.63; P < 0.05) and eugenol (r ¼ 0.59;P < 0.05). Similarly, the woody-related term “toasted bread” issignificantly correlated to both the volatile phenol vector (r ¼ 0.78;P < 0.05) and eugenol (r ¼ 0.72; P < 0.05). Among these woody-related aromas, the whiskylactone vector as well as eugenol arepositively correlated to the quality scores given by regular con-sumers (r ¼ 0.67 and r ¼ 0.66; P < 0.05) although no significantcorrelation with experts' quality perception are observed as it canbe seen in Fig. 2.

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The leather aroma attribute characterising wines belonging toCluster 1 can be attributed to the highest 4-ethylphenol concen-trations found in these wines (Table 3). This fact is further sup-ported by significant correlations between the scores obtainedfrom the sensory panel description for the leather attribute andthe OAV of the ethylphenol vector (r ¼ 0.59; P < 0.05). It isinteresting to note that this vector, which is formed by 4-ethylguaiacol and 4-ethylphenol, is positively correlated to thequality scores given by regular consumers (r ¼ 0.51; P < 0.1) as itcan be observed in Fig. 2. This result is interesting since the roleplayed by this compound is controversial on consumers' percep-tion. There are works that have attributed to the leather/animalcharacter imparted by this vector, especially by 4-ethylphenol, anegative role linked to low quality wines (Ferreira et al., 2009;Lattey et al., 2010). Similarly, the leather character imparted bythis compound has been found to be disliked by most consumersin a study conducted with Australian participants (Bramley et al.,2007), what could probably have led to a decrease in qualityperception given the correlation found between overall liking andquality perception (Delgado & Guinard, 2011). However, the pos-itive or negative effect on consumers' perception of the leatherycharacter (generally called Brettanomyces character) imparted bythis compound seems to be unclear and dependent on both con-sumers' expectations and matrix composition (Frost & Noble,

ive compounds influencing wine experts' and consumers' perceptionttp://dx.doi.org/10.1016/j.lwt.2014.09.026

Table 4Concentration ranges and median values of nonvolatiles found in the set of 12 wines (all expressed in micrograms per litre). Sensory threshold for astringency; discriminatorypotential (DP) calculated as the quotient betweenmaximum andminimum concentrations (DP > 2marked in bold) and correlation coefficients (r) of linear regression betweenthe concentration of each compound and quality evaluated by experts and consumers.

Compounds Concentration ranges Median Sensory thresholda DP r (expert Q)d r (consumer Q)e

Acids and derivativesCis-aconitic acid 0.08e0.65 0.24 0.1 (1) 8.15 ¡0.15 ¡0.87**Trans-aconitic acid 1.50e3.72 2.62 0.1 (1) 2.48 0.10 ¡0.07Protocatechuic acid 2.09e7.25 3.82 32 (1) 1.13 0.02 0.45Vanillic acid 5.45e6.89 6.03 53 (1) 1.00 �0.49 �0.04Syringic acid 1.20e1.70 1.40 52 (1) 1.00 �0.19 �0.01Protocatechuic ethyl ester 0.10e0.70 0.29 9 (1) 1.00 �0.24 0.64**Ellagic acid 4.04e50.30 13.61 1.99 (2) 12.5 ¡0.14 ¡0.22

Hydroxycinnamic acidsTrans-gallic acid 20.6e99.57 78.83 50 (1) 9.96 0.28 �0.25Cis-caftaric acid 1.83e6.32 3.86 5 (1) 6.32 0.32 �0.17Trans-caftaric acid 12.49e180.99 56.18 5 (1) 14.5 ¡0.23 ¡0.31Cis-caffeic acid 1.13e8.16 4.32 13 (1) 3.14 �0.41 �0.16Trans-caffeic acid 1.52e40.49 6.30 13 (1) 26.66 �0.62** �0.43Cis-coumaric acid 1.12e6.67 2.54 23 (1) 1.45 0.17 �0.25Trans-coumaric acid 0.07e0.81 0.20 23 (1) 1.00 0.30 �0.31Trans-coutaric acid 10.87e119 62.26 10 (3) 10.9 0.18 ¡0.56*Cis-ferulic acid 0.41e3.42 1.18 13 (1) 1.31 �0.41 �0.55*Trans-ferulic acid 0.01e0.33 0.17 13 (1) 1.00 �0.23 �0.18

FlavanolsProcyanidin B1 0.61e8.39 1.84 139 (1) 1.00 �0.41 �0.55*Epigallocatechin (EGC) 0.05e0.48 0.14 159 (4) 1.00 �0.23 �0.18Catechin 0.52e2.09 1.16 119 (4) 1.00 �0.60** �0.33Procyanidin B2 0.44e3.11 0.81 110 (1) 1.00 �0.41 �0.69**Epicatechin 0.53e4.26 1.05 270 (4) 1.00 �0.55* �0.48Epicatechin gallate <0.04 0.04 115 (4) 1.00 �0.35 0.20Epigallocatechin gallate <0.04 0.04 87 (4) 1.00 0.00 0.00Procyanidin A2 0.02e0.52 0.15 110c 1.00 �0.52* �0.56*Gallocatechin 0.19e1.77 0.42 165 (4) 1.00 �0.16 �0.28Total PAs 116e884 345 12 (5) 7.6 �0.11 �0.49Protein-precipitable PAs 0.27e0.60 0.43 na �0.42 0.53*

FlavonolsQuercetin-3-O-galactoside 0.02e0.25 0.09 0.2 (1) 6.30 0.23 �0.10Quercetin-3-O-glucuronide 0.01e10.0 4.11 1 (1) 49.9 0.26 ¡0.53*Quercetin-3-O-glucoside 0.02e0.31 0.07 0.1 (4) 15.5 0.06 �0.31Kaempferol-3-O-glucoside 0.03e0.11 0.07 0.3 (4) 1.90 0.26 �0.35Syringetin-3-O-glucoside 0.22e0.67 0.46 0.1 (1) 3.10 ¡0.26 ¡0.64**Isorhamnetin-3-O-glucoside 0.03e0.31 0.09 1.1 (1) 1.40 �0.06 �0.34Syringetin-3-O-galactoside 0.35e1.10 0.61 0.2c 27.4 ¡0.39 ¡0.62**Myricetin 0.03e0.12 0.09 10b (6) 1.00 0.15 0.19Quercetin 0.03e1.92 0.31 10b (6) 1.00 �0.14 0.02Kaempferol 0.03e0.16 0.03 20c (6) 1.00 �0.19 0.01Isorhamnetin 0.03e0.18 0.09 20c 1.00 �0.26 �0.19

a Reference in which the sensory threshold value has been calculated is given in brackets (na: not available). 1: Hufnagel and Hofmann (2008), 2: Glabasnia and Hofmann(2006), 3: Okamura and Watanabe (1981), 4: Scharbert and Hofmann (2005), 5: Rossi and Singleton (1966), 6: Dadic and Belleau (1973). Sensory thresholds determined inwater, except for those in (6), which are determined in 5% ethanol. The sensory procedure used is the half-tongue test except in references (3) and (6), which were determinedby paired/triangular and paired comparison tests, respectively.

b Sensory threshold for both bitterness and astringency.c For procyanidin A2, syringetin-3-O-galactoside and isorhamnetin the sensory thresholds of procyanidin B2, quercetin-3-O-galactoside and kaempferol have been

considered, respectively.d Correlation coefficient of linear regression with quality evaluated by experts (significant correlations: *P < 0.1; **P < 0.05).e Correlation coefficient of linear regression with quality evaluated by consumers (significant correlations: *P < 0.1; **P < 0.05).

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2002). In this context, higher polyphenolic contents result in alower perception of the leather character of wines (Petrozziello,Asproudi, Guaita, Borsa, Motta and Panero, 2014), which iscertain the case of wines of Cluster 1 (present significantly higherlevels of IPTs than the rest of the studied wines; P < 0.01,F ¼ 28.13). Similarly, recent findings obtained in our laboratory(unpublished data) show that a dearomatised wine spiked withhigh amounts of 4-ethylphenol (found in red wines) wasperceived lower in the leather attribute in the presence of woody-related aromas such as methyl and ethyl vanillates. These factssuggest a possible masking/suppressor effect generated by bothpolyphenols and woody-related aromas (generated by vanillates)on the ethylphenol character of wines of Cluster 1. All this couldlead to the controversial role attributed to these compounds onwine quality/liking perception (Wedral, Shewfelt, & Frank, 2010).

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Wines belonging to Cluster 2 present higher concentrations ofthe banana-smelling isoamyl acetate than wines of Cluster 1(Table 3) with median OAVs almost twice higher than in theSpanish wines (OAV ¼ 12 vs OAV ¼ 7). This could partially explainthe fruity character attributed to these wines. Naively, it was ex-pected that wines with higher fruity character (Cluster 2) wouldalso present higher concentrations of fruity ethyl esters and ofother aroma compounds related to fruit perception, such as nor-isoprenoids. Contrary to our expectations this is not certainly thecase and so far, wines of Cluster 1 present higher concentrations oflinear ethyl esters such as ethyl butyrate and of ethyl hexanoatethan wines in Cluster 2 as observed in Table 3. This rather sur-prising result may be caused by multiple factors studied atdifferent levels in the scientific literature. It could be firstexplained in terms of a probable suppression/masking effect of the

ive compounds influencing wine experts' and consumers' perceptionttp://dx.doi.org/10.1016/j.lwt.2014.09.026

Fig. 1. Projection of aroma descriptors and wines in the Correspondence Analysis (CA) space (dimensions 1 and 2). The arrows (illustrative variables) show the projection of thequality scores given by experts (expert quality) and regular consumers (consumer quality).

M.-P. S�aenz-Navajas et al. / LWT - Food Science and Technology xxx (2014) 1e12 9

fruity esters by oak-related compounds. The dominance of thequalitative woody feature generated by whiskylactones or guaiacolin binary mixtures over wine odorants with fruity character suchas isoamyl acetate or ethyl butyrate has been demonstrated at both

Fig. 2. Principal Component Analysis (PCA) biplot (dimensions 1 and 2). Fourteen aroma vecboth experts and consumers are projected as illustrative variables.

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iso-intense and peri-threshold odorant concentrations (Atanasovaet al., 2005). A second potential cause could be the higher levels inwines from Cluster 1 of 4-ethylphenol and acetic acid, both ofwhich have been shown to act as strong suppressors of the fruity

tors and four individual compounds are active variables and the quality scores given by

ive compounds influencing wine experts' and consumers' perceptionttp://dx.doi.org/10.1016/j.lwt.2014.09.026

M.-P. S�aenz-Navajas et al. / LWT - Food Science and Technology xxx (2014) 1e1210

note (San Juan et al., 2011). The third potential effect, for whichthere is not supporting literature, would be related to the highestlevels in wines of Cluster 1 of compounds such as ethyl acetate(Table 3), which could have a suppression/masking effect of thefruity character of esters.

Concerning wine CdR-102 (characterised by cat urine andperspiration attributes), it has concentrations in chemical aromaswith known pleasant character comparable to those of the wines ofthe two other clusters such as norisoprenoids or ethyl esters andhas even the highest concentration of the banana-like odorantisoamyl acetate (Table 3). This wine has maximum OAV values forthe fusel alcohol vector (formed by 2-methylpropanol and isoamylalcohol), which is barely three units higher than the median value(OAV ¼ 11). Interestingly, this vector is negatively correlated to thequality scores of consumers (r¼�0.67; P < 0.05). However, the lowOAV differences among clusters suggest that it is not very probablethat this vector is the major responsible for the unpleasant char-acter of this sample. The major recorded chemical difference is thatit has maximum values (OAV ¼ 151) in the branched fatty acidvector, being almost 40 units higher than the median value(OAV ¼ 114) calculated among the 12 wines, which ranges from 94to 151. Although this vector is formed by fatty acids described withterms such as “rancid” or “unpleasant” (Escudero et al., 2004), itcannot be concluded that these compounds are exclusivelyresponsible for such negative attributes. First because the differ-ence in terms of OAVs is not that large (just a 25% overall increase,nearly a 45% for 3-methylbutyric acid), and second because in factthese compounds have been found to play a positive role on theperception of the fresh fruit character (San Juan et al., 2011) as theyhave been shown to exert a masking effect on animal-leather notes(Romano, Perello, Lonvaud-Funel, Sicard, & de Revel, 2009).Therefore, although it is true that the levels of these compounds inthe CdR-102 sample are much higher than those reported inreference (San Juan et al., 2011), which would support a potentialeffect of these compounds on the negative aroma properties of thesample, we cannot rule out the existence of another aroma com-pound, not quantified in this study, responsible for those aromanotes.

The importance of sensory interactions in the perception ofnearly all aroma nuances is indirectly demonstrated by the lack ofsuccess in building satisfactory predictive models for the differentaroma nuances, using linear regression-based algorithms. So far,only for the specific cherry descriptor (rich inwines from Cluster 2),a satisfactory PLS-regression model could be obtained (explaining92% of the original variance �86% by cross-validation- with anRMSE of 0.515). The model explains this specific aroma nuance asthe result of the interaction between norisoprenoids (positive ef-fect), whiskylactone and the sum of both branched and linear fattyacid vectors (negative effects). The norisoprenoid vector contrib-utes positively to the cherry model in agreement with previousresults observed for the fruity character (Escudero, Campo, Fari~na,Cacho, & Ferreira, 2007). On the contrary, both the whiskylactoneand fatty acid vectors have a negative contribution to the cherrycharacter, suggesting a masking/suppression effect of the red fruityaroma compounds by these two vectors.

Concerning the correlation between quality and the aromacompounds or groups of compounds involved in the cherry aromamodel, the whiskylactone phenol vector is positively correlated toconsumers' quality perception (r ¼ 0.56; P < 0.1), while no signif-icant simple correlation was found between quality and the nor-isoprenoid or fatty-acid vectors. Although the fruity-like isoamylacetate does not emerge as a significant variable in the red fruitaroma of the studied wines, it is interesting to note that it presentsa negative and important correlation with the quality (r ¼ �0.65;P < 0.05) evaluated by consumers.

Please cite this article in press as: S�aenz-Navajas, M.-P., et al., Sensory-actof red wine intrinsic quality, LWT - Food Science and Technology (2014), h

3.4. Non-volatile compounds explaining wine astringencydifferences and quality perception

The astringency evaluated by the trained panel shows a highlysignificant difference (F ¼ 5.56; P < 0.01) between wines accordingto one-way ANOVA analysis. Wines CdR-936 and RJ-917 are themost and less astringent, respectively. Although no significantcorrelation is observed between the sensory astringency evaluatedby the trained panel and experts' quality scores, this correlation(r ¼ �0.43; P ¼ 0.103) is negative and close to significance forconsumers' quality assessments.

The study of the non-volatile compounds with astringentproperties in the 12 red wines provides quantitative data for 39compounds and groups of compounds with known astringencyproperties (Table 4). Non-volatile molecules with astringent char-acter have not shown clear additive effects, unlike volatile com-pounds, and so far, even masking effects have been observedamong them (S�aenz-Navajas et al., 2012). Thus, vectors have notbeen constructed with the non-volatile molecules studied.

The potential ability of non-volatile molecules to induce sensorydifferences among wine samples is studied calculating the quotientbetween the maximum and minimum concentrations (calleddiscriminatory potential -DP-). This quotient (for DP > 2) revealsthat five flavonols (quercetin-3-O-glucuronide, syringetin-3-O-galactoside, quercetin-3-O-glucoside, quercetin-3-O-galactoside,syringetin-3-O-glucoside), four acids (ellagic, trans-gallic, cis-aco-nitic and trans-aconitic acids), five hydroxycinnamic acids (trans-caffeic, trans-caftaric, trans-coutaric, cis-caftaric, and cis-caffeicacids) and the total content of PAs are at concentration ranges largeenough to cause sensory differences among the studied wines.Except for cis-caffeic acid, which is present at concentrations lowerthan its sensory threshold, the rest of compounds cited above areexpected to have a significant sensory effect on wine. The DoTs ofquercetin-3-O-glucoside (r ¼ 0.83; P < 0.001), trans-aconitic acid(r ¼ 0.65; P < 0.05), total PAs (r ¼ 0.59; P < 0.05) and to a lesserextent quercetin-3-O-galactoside (r¼ 0.53; P < 0.1) are significantlycorrelated to the sensory astringency evaluated by the trainedpanel of 56 assessors. The sensory activity of these compounds andtheir involvement in the sensory astringency of red wines has beenalready suggested and confirmed by addition experiments (S�aenz-Navajas et al., 2012).

Table 4 shows important negative correlations between con-sumers' quality scores and certain compounds with astringent ac-tivity. These compounds are cis-aconitic acid (r¼�0.87; P < 0.001),syringetin-3-O-glucoside (r ¼ �0.65; P < 0.05), syringetin-3-O-galactoside (r¼�0.62; P < 0.05), and to a lesser extent quercetin-3-O-glucuronide (r ¼ �0.53; P < 0.1) and protein-precipitables PAs(r ¼ �0.53; P < 0.1). On the contrary, no significant correlationbetween the quality scores given by experts and compounds withastringent activity are shown.

4. Conclusions

For wine consumers, high quality is linked to wines with higherconcentrations of oak-ageing-related compounds such as eugenoland E� and Z-whiskylactones and, surprisingly, the leather-like 4-ethylphenol. On the contrary, fusel alcohols and astringent-relatedcompounds such as PAs, cis-aconitic acid, certain flavonols andhydroxycinnamic acid derivatives are linked to wines scored lowerin quality by consumers. In contrast, wine experts perceive winesdescribed mainly with red fruity aromas higher in quality and thushigher in norisoprenoids and lower in whiskylactones and volatilephenols. Although sensory judgements of wines are traditionallycarried out by experts or winemakers, results suggest that moresensory studies with assessors with different levels of expertise are

ive compounds influencing wine experts' and consumers' perceptionttp://dx.doi.org/10.1016/j.lwt.2014.09.026

Experts' quality scores Consumers' quality scores

CdR-081 3.18 ± 0.16 2.22 ± 0.14CdR-102 1.69 ± 0.14 1.28 ± 0.14CdR-333 1.87 ± 0.16 3.03 ± 0.13CdR-888 2.44 ± 0.15 2.59 ± 0.16CdR-903 3.03 ± 0.14 2.26 ± 0.16CdR-936 2.37 ± 0.17 1.78 ± 0.14RJ-005 1.91 ± 0.17 2.87 ± 0.17RJ-058 3.00 ± 0.17 2.50 ± 0.17RJ-381 2.46 ± 0.15 2.17 ± 0.15RJ-690 2.92 ± 0.15 2.98 ± 0.16RJ-774 2.17 ± 0.13 3.31 ± 0.14RJ-917 2.76 ± 0.20 2.87 ± 0.16

M.-P. S�aenz-Navajas et al. / LWT - Food Science and Technology xxx (2014) 1e12 11

needed in order to reveal the main sensory-active compoundsrelated to wine quality perception and further gaining marketshare.

Acknowledgements

The research reported in this article was financially supportedby the Bourgogne council, the Spanish Ministry of Education(AGL2010-22355-CO2-01/02) and Instituto de Estudios Riojanos.M.P.S.N. and J.M.A. acknowledge the Spanish Education Ministry(M.E.C.) for her postdoctoral fellowship and Navarra Governmentfor his predoctoral grant, respectively. Authors also want to thankMarivel Gonzalez-Hernandez and Christelle Pecher from the Uni-versities of La Rioja and Burgundy, respectively, for their support inthe panel training sessions, Patrick Vuchot from Inter-Rhone (Or-ange and Avignon, France) for his support during the experimen-tation with consumers and experts, and panellists for their interestand diligence during their participation in the sensory sessions.

Appendix A. Quantification of individual non-volatilecompounds by UPLC-MS

UPLC analyses were performed using a Waters Acquity UltraPerformance LC system (Milford, MA, USA) by direct injection ofwine samples, previously filtered with 0.22 mm nylon discs. UPLCseparation was achieved using an acquity BEH C18 column(100 mm� 2.1 mm, i.d., 1.7 mm particle size, Waters), kept at 40 �C.Mobile phase flow rate was 0.45 mL min�1 and the injection vol-ume was 7.5 mL. Solvents were (A) water/formic acid (0.1%), and (B)acetonitrile/formic acid (0.1%). The gradient programme employedwas as follows: 0e4 min, 99e92% A; 4e11 min, 92e70% A;11e13.5 min, 70e0% A; 13.5e14.5 min, 0e99% A. The UPLC systemwas coupled to a microTOF II high-resolution mass spectrometer(Bruker Daltonik, Germany) equipped with an Apollo II ESI (elec-trospray) source and controlled by Bruker Daltonics DataAnalysissoftware. The ESI source was operated in negative mode recordedin the range of m/z 150 and 1500. The optimised conditions of theESI source were as follows: capillary voltage, 3.5 kV; ESI sourcetemperature, 180 �C; desolvation temperature, 200 �C; cone gasflow, 9 L min�1; the nebulizer gas was set at 3 bar and 25 �C. LCeMSwas performed operating in both (continuum)MSmode and in MS/MS mode. The spectra were acquired at the speed of 2 scan/second.Fragmentor voltage for MS/MS acquisition mode was 35 eV.

The identity assignation of compounds was carried out bycomparison of their retention time (tR), MS andMS/MS spectrawiththose of their respective commercially available standards. Quan-tificationwas performed by UPLC-MS and using the response (peakarea) ratio for each compound in duplicate. The calibration curveswere prepared by calculating the peak area of the different stan-dards and were used to determine linearity and instrumentaldetection (LOD) and quantification (LOQ) limits. LOD and LOQ werecalculated using 3 S/m and 10 S/m (S is the standard deviation ofthe response; m is the slope of the calibration curve), respectively.Duplicated calibration curves were prepared in methanol. Theestimated concentration of coutaric acid has been expressed ascaftaric acid equivalents (mg L�1 of caftaric acid) and ferulic acid ascaffeic acid equivalents (mg L�1 of caffeic acid).

Appendix B. Recruitment of regular consumers and expertsfor quality evaluation

Regular consumer's recruitment: To qualify participants had tobe of legal drinking age (18 years), to consume red wine at leasttwice a month and they could not be professionally linked to wine.Besides, participants had to live either in La Rioja (for the Spanish

Please cite this article in press as: S�aenz-Navajas, M.-P., et al., Sensory-actof red wine intrinsic quality, LWT - Food Science and Technology (2014), h

recruitment) or Avignon (for the French recruitment) areas for atleast the last 10 years in order to guarantee their immersion in eachof both wine cultures and thus their familiarity with the wines ofthe region. The screening and selection process ensured similar ageand gender quotas of consumers between both countries. Althoughexact matching proved difficult, a chi-square test showed no sig-nificant difference between both groups of consumers in terms ofage and gender distribution. Fifty-six Spanish consumers (51.8%men and 48.2%women from 19 to 67 years, median¼ 39.5) living inLa Rioja area and fifty-two French consumers (48.1% men and 51.9%women from 19 to 67 years, median ¼ 42.5) living in Avignon areatook part in the study.

Expert's recruitment. The panels of experts included establishedwinemakers, wine-science researchers and teaching staff (regularlyinvolved in wine-making and/or wine evaluation). The Spanishpanel was composed of fifty-nine wine professionals from theDOCa Rioja area (51% men and 49% women, from 24 to 56 years,median ¼ 35) and the French one of sixty wine experts from theCotes du Rhone area (67% men and 33% women, from 22 to 72years, median ¼ 45). One-way ANOVA calculated on the age ofparticipants and their country of origin as independent variableshowed that French experts were significantly older than Spanishexperts (F ¼ 9.15; P < 0.05). Gender distribution was not signifi-cantly different according to chi squared tests (P > 0.05) betweenFrench and Spanish experts.

Appendix C. Quality ratings (±standard mean error) for the12 studied wines obtained from the categorisation taskaccording to four levels of quality (“very high” scored as 4,“high” as 3, “low” as 2 and “very low” as 1) carried out by wineexperts and regular consumers.

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