Introduction

For the past 10 years the production and consumption of Rosé wines has continued to progress worldwide (Pouzalgues et al., 2013). France is the world’s leading producer (7.6 million hectoliters produced in 2014) and consumer (8.1 million hectoliters consumed in 2014) of Rosé wines (OIV, 2015). However, these wines suffer from the lack of a unique, shared definition and are often mixed together with red wines in economic data (OIV, 2015).

In this context, wine producers are seeking to increase the quality of Rosé wines and identify their specificity better (Masson and Schneider, 2009). The technicality and know-how of the wine producers from a specific area can lead to a qualitative development and specific characteristics of wines related to a specific “terroir” called typicality (OIV, 2010). The value of the wines, as perceived by consumers, can be increased by a strong link with a territory conferring a specific typicality (Boncinelli et al., 2016). Using scientific methodologies to enhance knowledge of the intrinsic attributes of typicality can lead to better communication about wines (Passebois-Ducros and Trinquecoste, 2013) and thus increase consumer satisfaction.

Only a few recent scientific works have focused on Rosé wine characteristics. They studied the composition (more specifically the volatile compounds), color, and sensory characteristics as well as the preference of wine professionals. Their results indicated a large diversity in flavor and color among Rosé wines. Relationships between sensory profiles and wine composition have been explored for Rosé wines (Wang et al., 2016a). These authors investigated the characteristics of Australian Rosé wines for the first time. They were described by ‘green’ and ‘citrus’ aromas with some ‘tropical fruit’ and ‘floral’ aromas. Some wines were ‘oaky’ and ‘spicy’ but with ‘fruity’ and ‘floral’ characters. Some wine professionals preferred Rosé wines with less residual sugar, which exhibited ‘red fruit’, ‘floral’, ‘confectionery’ and ‘honey’ characters (Wang et al., 2016b). Masson and Schneider (2009) identified several major volatile compounds in the French Provence Rosé wine aroma as being responsible for some sensory characteristics. Additionally, Rosé wines are well known for their color. A wide range of colors is observed in Rosé wine, both in France and all over the world. Color measurement in a large sample set of Rosé wines from several countries showed colors from very pale to more saturated red. Within French Rosé wines, disparities of color appear from one area to another. An analysis of the international Rosé wine collection (Centre du Rosé & Union des Œnologues de France) showed that Provence Rosé wines are among the clearest in the world while Rosé wines from Bordeaux and South-West France are darker and redder.

Despite some identified sensory characteristics, no study has been conducted on the typicality of Rosé wines. By contrast, numerous works have focused on the typicality of other wines (Cadot et al., 2012; Canuti et al., 2017; Gomes et al., 2016; Llobodanin et al., 2014; Loison et al., 2015; Perrin and Pagès, 2009). In these works, authors evaluated the typicality of specific red or white wines from different Protected Designation of Origin (PDO) or grape varieties. They aimed to identify the main sensory characteristics of typical or not typical wines in a category or find the chemical composition related to typicality. None of them included the impact of color on the perceived typicality.

This typicality concept is supported by the existence of a common memorized prototype, which represents the image of all the previous experiences of the wine type (Casabianca et al., 2006; Maitre et al., 2010). The typicality assessment method proposed by Ballester et al. (2008) has been used by several authors (Loison et al., 2015; Perrin and Pagès, 2009). It is based on the evaluation of the product by connoisseurs, generally wine professionals from diverse backgrounds (Loison et al., 2015). This methodological approach can detect if a group of tasters share the same perception of typicality for a specific category of products and identify the sensory attributes and chemicals related to it.

In general, research on typicality has focused on flavor perception and has been performed using dark INAO glasses. However, Cadot et al. (2010) and Perrin and Pagès (2009) worked with transparent glasses and found a significant impact of color intensity on the typicality of wines from Anjou Village Brissac. Recently, Valentin et al. (2016) worked with dark and clear glasses in order to identify the impact of color on the judgment of wine quality and typicality. They concluded that color characteristics (hue and intensity) were independent of quality and typicality in the case of Pinot Noir from France and New Zealand.

The aim of the present work was to characterize the typicality of Provence Rosé wines. When disregarding color by using black glasses, is the identity of Provence Rosé wines perceptible by tasting and shared by professionals due to specific odors and aromas? Then, what is the impact of tasting wine in a transparent glass; in other words, what is the impact of color on wine typicality? For this, complementary methods were used: exemplarity measurements, sensory evaluation and color assessment.

Materials and methods

Results

1. Typicality assessment: consensus among wine professionals

1.1 Evaluation in dark glass

The professionals of Provence shared a common perception of the Provence Rosé wine exemplarity based on odor and flavor, when wines were served in a dark glass. The agreement among the 41 tasters evaluating 41 wines was high. The loading of the assessors on the two principal components (PC1 and PC2) of the PCA performed on the typicality scores is shown in Figure 1a. These principal components explain 36.7% and 7.7%, respectively, of the total variance. All the professional tasters were loaded on the positive side of the first axis, highlighting the fact that they tended to score the typicality of the wines in a similar way. The low proportion of total variance explained on the first components was due to the use of many wines and tasters. Kendall’s coefficient of concordance was 0.33 (p-value < 0.001), the ICC(C) was 0.31 and the one considering the agreement, ICC(A), was 0.28 showing a good consensus among wine professionals (Loison et al., 2015).

The 41 selected wines were perceived with very different levels of exemplarity. The average typicality scores for each wine varied between 0.9 and 6.7, with an overall average for all wines of 4.04 (Table 2). All the Provence Rosé wines (PRO) had a level of exemplarity higher than this overall average. Five of the seven Provence wines were the five best examples. The other two were among the twelve best examples. These results were observed when analyzing the averages per area. The average and LSD groups were: PRO: 6.1 (a), LAN: 4.9 (b), VDL and BDX: 3.7 (c), BGC: 3.3 (cd) and RHO: 2.5 (d).

Figure 1. Correlation circles of Principal Component Analysis presenting the typicality assessment between judges in dark glass (a) and transparent glass (b).

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Table 2. Averages of the exemplarity scores evaluated by 41 professionals (scores between 0 “very bad example of a Provence Rosé wine” and 10 “very good example of a Provence Rosé wine”) and CIELAB color coordinates (L, a, b, C and H) of the 41 Rosé wines studied.

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Wines	Exemplarity Average (n=41)			Color measurements
	In Dark Glass	In Transparent Glass	Difference between both	L	a	b	C	H
PRO5	6,69	7,06	0,37	97,5	2,0	4,7	5,1	67,4
PRO2	6,69	7,40	0,72	96,9	2,9	4,4	5,3	56,5
PRO4	6,65	7,32	0,67	97,3	2,5	3,9	4,6	57,1
PRO7	6,41	7,02	0,60	96,3	2,8	5,8	6,4	64,4
PRO6	6,41	7,04	0,63	96,8	2,2	5,6	6,0	68,5
LAN6	6,27	7,19	0,91	96,0	3,7	5,3	6,5	55,4
LAN4	5,57	6,09	0,52	93,9	7,0	6,0	9,2	40,9
BDX6	5,40	6,14	0,74	94,3	6,0	6,7	9,0	48,0
BDX3	5,24	5,16	-0,08	89,6	11,4	8,9	14,5	38,1
PRO1	5,05	5,63	0,58	94,1	5,4	7,8	9,5	55,6
BGC5	4,89	5,71	0,82	95,1	5,4	5,9	8,0	47,8
PRO3	4,85	5,45	0,60	95,8	4,7	5,4	7,1	48,7
LAN2	4,85	5,42	0,57	94,6	5,5	4,9	7,4	41,6
VDL5	4,74	5,02	0,29	92,0	9,1	6,6	11,2	36,3
LAN1	4,66	4,63	-0,03	91,7	9,2	7,8	12,0	40,2
LAN3	4,55	4,93	0,38	92,2	8,8	7,8	11,7	41,5
LAN7	4,34	5,23	0,89	96,3	3,0	6,6	7,2	65,4
VDL2	4,26	4,36	0,10	91,9	7,6	9,3	12,0	50,7
LAN5	4,26	3,78	-0,48	89,4	12,4	8,6	15,1	34,9
BGC2	4,24	5,50	1,26	94,6	5,0	7,5	9,0	56,5
VDL7	4,17	5,21	1,04	94,8	5,0	4,9	7,0	44,8
VDL3	3,75	3,69	-0,06	90,4	10,8	7,4	13,1	34,6
BGC3	3,40	3,84	0,44	93,5	7,2	8,2	10,9	48,6
RHO3	3,37	2,05	-1,32	84,4	17,8	11,7	21,3	33,3
VDL1	3,35	3,84	0,49	92,2	7,8	7,9	11,1	45,4
BDX1	3,35	2,69	-0,66	88,6	14,7	8,4	16,9	29,7
VDL4	3,28	3,95	0,67	92,2	8,0	7,9	11,3	44,6
BDX7	3,09	2,26	-0,83	86,8	14,0	12,3	18,7	41,3
RHO1	3,01	1,64	-1,38	83,3	18,3	10,9	21,3	30,7
BGC4	3,01	2,43	-0,58	89,4	11,7	10,8	15,9	42,9
BGC7	2,97	2,10	-0,87	86,3	17,6	8,8	19,7	26,7
RHO2	2,91	1,71	-1,20	80,9	23,8	8,8	25,4	20,4
RHO6	2,84	2,40	-0,44	84,4	17,7	11,0	20,8	31,8
BDX5	2,81	2,77	-0,04	89,5	13,3	7,8	15,4	30,3
VDL6	2,43	1,94	-0,49	86,5	14,0	10,6	17,6	37,0
BGC1	2,31	2,06	-0,25	88,6	15,0	7,7	16,9	27,0
RHO5	2,30	1,18	-1,12	77,5	24,9	13,5	28,3	28,5
RHO4	2,29	1,44	-0,85	81,3	21,5	12,5	24,8	30,2
BGC6	2,16	1,62	-0,54	88,6	15,7	6,3	16,9	22,0
BDX4	2,09	1,38	-0,71	82,0	22,5	13,8	26,4	31,5
RHO7	0,86	0,59	-0,27	76,8	27,3	13,7	30,6	26,7

1.2 Evaluation in transparent glass

There was an impact of color on the judgment of exemplarity. When wines were poured into transparent glass, the perceived typicality of the majority of the wines changed in different ways. Eleven wines had a decrease in exemplarity of more than 0.5 while fifteen wines had an increase of more than 0.5. Interestingly, no wines with an exemplarity mean score in dark glass of more than 5 received a lower score in transparent glass, except BDX3 (-0.08). In parallel, no wines with a mean score of less than 3 in dark glass had a higher score in transparent glass. In the range between 3 and 4, the exemplarity increased, decreased or stayed stable depending on the wine. In relation to these observations, there was a high correlation (R = 0.96) between the average typicality score in dark and transparent glass.

As for the evaluation in dark glass, a PCA with judges as variables and wines as individuals was carried out (Figure 1b). The consensus was higher with transparent glass as indicated by the higher percentage of inertia explained on the first two components (60.56% for clear glass against 44.42% for dark glass). The Kendall’s coefficient, the ICC(C) and the ICC(A) also indicated a higher agreement among subjects: 0.53, 0.5 and 0.46, respectively.

Discussion

This work aimed to identify the sensory typicality of one specific area, here Provence, compared to other French areas based on odor, aroma and mouth feel characteristics, first without the influence of color (blind tasting in dark glass) and then with color as indicator (clear glass). Wine professionals from this area shared a common perception of which wines are typical or not typical from a flavor point of view. They agreed about the characteristics that Provence Rosé wines should have. In the literature, the consensus observed among tasters from a specific area varies depending on the number of products tested, the number of tasters and the sensory space selected to analyze the perceived exemplarity. It also depends on the sensory variability in the studied area and the existence of a shared perception of typicality. With 41 wines and 41 tasters, the present study has the largest number of products and tasters reported in the literature. This could have had an impact on the statistical indicators, artificially decreasing the percentage of inertia on the PCA due to a higher number of components. However, the percentage of inertia explained on the first two components (44%) and other coefficients such as ICC(A), ICC(C), and Kendall’s coefficient were high indicating high agreement among tasters. Thus, there is a consensus among the professionals; they have a common representation of what Provence Rosé wines should be.

Based on the sensory characteristic of each wine or clusters and sub-clusters, there is no single sensory profile of Provence Rosé wines. The more typical wines were included in different clusters of the HAC performed on the sensory attribute scores. Nevertheless, the present methodology and the PLS coefficients enabled the sensory attributes impacting the Provence Rosé wine typicality to be identified and hierarchized. Fruity ‘citrus fruit’ and ‘exotic fruit’ odors and aromas as well as ‘fresh floral’ odors and aromas and ‘mineral’ aroma had a positive impact on the exemplarity of Provence Rosé wines. These positive attributes are in accordance with other studies about the main volatile compounds characterizing Provence Rosé wines (Masson and Schneider, 2009; Pouzalgues et al., 2013). These wines contained mainly ethyl esters and higher alcohol acetates at the origin of the ‘fruity’ and ‘amylic’ notes; two thiols: β-mercaptohexanol (3MH) and 3-mercaptohexyl acetate (A3MH) linked to ‘citrus’ and ‘exotic fruit’ notes; two furanic compounds: furaneol and homofuraneol having a synergic action for ‘fruity’ and ‘caramel’ notes (Ferreira et al., 2002); and one C13 norisoprenoid: β-damascenone having a positive influence on ‘dried fruit’, ‘black fruit’ and ‘roasted’ notes and exerting a suppressing effect on ‘red fruit’ (Callejón et al., 2012; Ferreira et al., 2016). It would be interesting to analyze the volatile compounds from the wines of the present study in order to identify those markers responsible for ‘dried, roasted and red fruit’. In fact, some other attributes penalized the exemplarity: the fruity attributes ‘red fruit’ and ‘ripe fruit’ odors, the ‘dried fruit’ odor as well as ‘smoked’ and ‘spicy’ aromas.

The typicality of Provence Rosé wines perceived by professionals is based mainly on the odor and aromatic characteristics. In fact, no mouth feel and taste attributes were significant in the PLS. Cluster C3 was ‘sourer’ and ‘astringent’, but wines with more ‘amylic’ and ‘citrus fruit’ notes were more typical (C3.2.). This does not mean that these characteristics are not important in Provence Rosé wines, but aromatic characteristics dominate. Finally, it seems that two other sensory properties (‘alcohol’ and ‘balance’) could play a role in the exemplarity score even if these were not significant in the PLS. Products in group C3.2 had a higher ‘alcoholic’ sensation while in group C2.3, LAN4 and PRO2 were perceived as more ‘alcoholic’ and had higher exemplarity scores than other wines in this group. On the other hand, professionals penalized ‘unbalanced’ wines (e.g. RHO5). The concept of ‘balanced’ or ‘unbalanced’ wine is an abstract quality little used by expert panels but frequently mentioned by professionals (Picard et al., 2015; Valentin et al., 2016). These two characteristics should be investigated further in other experiments in order to better understand how to explain ‘balanced’ based on trained panel evaluation as well as the role of ‘alcoholic’ perception in Provence Rosé wine exemplarity.

Before discussing the impact of color on Provence Rosé wine typicality, it should be noted that even using dark glass there was a significant correlation between color parameters and typicality. Provence Rosé wines are characterized by their light color (Flanzy et al., 2009). Color analysis (CIELAB) revealed that the clearest wines were more typical whereas the reddest wines, i.e. with a high ‘a’ value (e.g. RHO1, BDX4 or RHO6), were the least typical. The color of Rosé wines is impacted by the date of harvest and the duration of the skin contact (Flanzy et al., 2009; Institut Technique de la Vigne et du Vin, 2006). In Provence, wines can be less colored due to i) the type of variety used: local varieties like Tibouren and Cinsault have less anthocyanins in their skin than other varieties, ii) the early date of harvesting during fresh conditions at nights and iii) the duration and temperature of the skin contact, lower in the Provence area (Flanzy et al., 2009). This enological process leads to a specific color that also impacts the aromatic characteristics due to a different volatile composition (Callejón et al., 2012). The same authors highlighted the highest concentration of β-damascenone for the highest skin contact duration while Lukić et al. (2017) observed the lowest concentration using cold-pre-fermentation but no results are available specifically for Rosé wines. β-Mercaptohexanol and 3-mercaptohexyl acetate, revealing ‘citrus’ notes, are mainly produced by yeast metabolism (Swiegers et al., 2007). Producers of Provence Rosé wines use cold temperatures before alcoholic fermentation to minimize oxidation and color extraction. In this way, there is a relationship between the odor and aromatic characteristics and the color obtained. Further scientific studies are needed to characterize the specificities of each area concerning the enological processes.

In relation to this result, the color of the wines clearly modified the perception of exemplarity when they were poured into transparent glasses. The color is an important part of Provence Rosé wine typicality, which increases following the decrease in luminance and in ‘a’ and ‘b’ color parameters. The significant increase in agreement among judges when wines were presented in clear glass shows how determinant this parameter is for Provence wine producers. Professionals from the Provence area agree that clear and more orange wines are more typical than darker and redder wines.

It has to be said that, for practical reasons, the methodology used was not totally satisfactory to underline this point. There was a high dependence between the two typicality scores since they were recorded one after the other by just changing the glass. To better estimate the contribution of color to the perceived typicality of Rosé wines, another experiment is proposed for the next study. It would be based on the methodology used by Valentin et al. (2016), who asked tasters to evaluate independently a set of wines served with different codes in both types of glass. Moreover, due to the observed correlation between aromatic and color characteristics, it would also be pertinent to use food colorant to change the color of wines and to make color and aromatic characteristics independent.

Finally, in such studies of PDO typicality, the selection of wines is determinant. Which wines are compared to answer which questions? As in Cadot et al. (2010) and Perrin and Pagès (2009), it would have been possible to focus on a selection of wines closer from a sensory point of view. In the present experiment, it was decided to select wines from the six main Rosé areas in order to represent better the diversity of sensory characteristics present on the French market. This gave access to how wine professionals from the same specific area define the main characteristics of their wines compared to others. The focus here was on the perception of Provence Rosé wine exemplarity by Provence wine professionals. The same experiment could be done with tasters from outside the area to identify if wine professionals from other areas in France share the same vision of Provence sensory characteristics. In addition, the same methodology could be used to ask professionals from other areas to evaluate the typicality of their area and, in this way, identify and communicate better about the different profiles of Rosé wines throughout France.

Further studies are also required to characterize the sensory properties of the wines. Characterization of wine typicality will include volatile composition considering Odor Activity Values that take into account odor and aroma sensory thresholds as suggested by Sáenz-Navajas et al. (2015).

Finally, it would be interesting to focus within a specific area and consequently identify the “terroir” specificity within the areas. The sensory results of the present study show some nuances among the seven Rosé wines selected as representative of the area’s diversity. However, for example this area is composed of sub-appellations (e.g. Lalonde, Sainte Victoire PDO). The same methodology carried out with wines and professionals from each sub-area would reveal if wines from specific areas have common and specific characteristics linked to “terroir” factors such as soil, climate, vine development, grape composition and enological practices. Like family members, they may have similar traits because they are from the same area, but they could be differentiated by some elements if professionals from a specific subarea share a common sensory definition of their wines due to “terroir” factors (soil, climate, etc.) or human factors.

Conclusion

This work is the first study of Rosé wine typicality on a national scale, focusing on the typicality of one of the most productive areas: the Provence area. The main objective was to see whether the exemplarity of Provence Rosé wines could be explained by criteria other than color. The results confirm that Provence Rosé wine typicality is based on color because the clearest wines are the most typical. Nevertheless, color is not the only factor; odors and aromas are also involved in the typicality of Provence Rosé wines. Wine professionals from Provence agreed on ‘citrus fruit’, ‘exotic fruit’ and ‘fresh floral’ odors and aromas being typical attributes sought in Provence Rosé wines. Beyond this overall result, there is no single sensory profile of typical Provence Rosé wines. The more typical wines are included in different clusters of the HAC performed on sensory attribute scores. This means that variability in the sensory profiles is observed within the areas with some common characteristics that distinguish Provence Rosé wines from other French Rosé wines.

Acknowledgements

The authors wish to thank all the assessors involved in wine evaluations and Clémence Salou, Aurélie Chevallier, Stéphane Miroufe, and Véronique Festino for their technical help. The research was funded by the Conseil Interprofessionnel des Vins de Provence, la Région Provence Alpes Côtes d’Azur and France Agrimer.

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