Abstract
Wine consumers are interested in components of red wines that contribute to health effects, flavor, and color. Three Vitis vinifera wines (Cabernet Sauvignon, Merlot, and Zinfandel) were blended using formulas created by an augmented simplex centroid mixture design, resulting in 10 wines (three single-component wines, three binary blends, and four tertiary blends). Compositional and color components of the wines were analyzed during 12 months of storage at 15°C with descriptive analysis and consumer evaluations at 30 days. Blending impacted compositional components and sensory profiles of the wines. During 12 months of storage, the blended wines exhibited similar compositional and color changes as their single counterparts; total anthocyanin content decreased and red color density and percent polymeric color increased. The primary anthocyanins detected in the wines by HPLC analysis were malvidin-3-O-monoglucoside, typical of V. vinifera. The anthocyanins decreased during storage with the formation of pyranoanthocyanins from condensation reactions. When descriptive and compositional analysis were compared, red color intensity and depth of color were correlated (r > 0.85) with clarity, flavor intensity, red color density, L*, chroma, total anthocyanin content, and polymeric color content. When consumer evaluations and compositional analysis were compared, consumer liking of appearance of the wines was positively correlated to red color density (r = 0.83), total anthocyanins (r = 0.85) and percent polymeric color (r = 0.93) and negatively correlated to L*(r = 0.99), chroma (r = 0.91), and hue (r = 0.99). Blending light-bodied wine with full-bodied wine positively affected consumer acceptance. Sensory and compositional data can be used to determine the overall impact of critical parameters for blending V. vinifera wines.
The wine industry and wine consumers have become more aware of the health benefits associated with consumption of red wine. The complexity of wine includes health components, such as alcohol, polyphenolics, antioxidants, vitamins, and minerals (Bravo 1998, Grønbæk 2001). Researchers have investigated the beneficial in vivo health effects of red wine consumption and found increased antioxidant levels, anti-cancer properties, and decreased low density lipoproteins (Rifici et al. 1999, Williams and Elliott 1997).
Phenolic components present in wine are critical to the health-related properties of wine consumption and are more prevalent in red wines than white wines because of the maceration and aging process of red wines (Williams and Elliott 1997). Flavonoids, a key group of polyphenolic compounds found in red winegrapes, protect humans against allergies, cancer, inflammation, ulcers, kidney damage, and heart disease (Bravo 1998). Two of the main types of flavonoids in red wines are anthocyanins and flavonols. Proanthocyanidins (a type of flavanol) also have a significant effect on wine aging and flavor potential.
In wine production, maintaining red wine color, flavor, beneficial attributes, and desirable sensory characteristics are critical. Phenolic compounds, flavonoids specifically, provide important sensory properties to wines including color, flavor, and astringency characteristics (Mazza et al. 1999). Proanthocyanidins, also called condensed tannins (flavan-3-ols), contribute to the perception of wine astringency, a desirable wine attribute for many consumers (Villamor et al. 2009). Color is also one of the most important indicators of wine quality to consumers (Parr et al. 2003). As wine ages, anthocyanins can react with tannins to form polymeric pigments, resulting in increased wine color stability that is dependent upon storage temperature and duration (Auw et al. 1996, Somers and Evans 1974).
Many studies have addressed color-related issues regarding polymeric pigments, from the sensory descriptors and consumer acceptance (Parpinello et al. 2009), characterization of color components (Versari et al. 2007), and anthocyanin transformation during aging (Wang et al. 2003). Correlation analyses between sensory and instrumental data are useful, particularly since instrumental measures are viewed as more consistent, reproducible, and less expensive, assuming that the instrumental analysis was done correctly.
The use of sensory evaluation in wine research is increasing in popularity, but can be expensive and time-consuming. Most studies use descriptive, or trained, panelists because of the smaller sample size and more objective research measurement. However, they do not provide preference or acceptance data, an important component of product development. Studies using consumers have the advantage of understanding which characteristics affect liking or how to specifically modify a product to meet consumer needs and wants. The objectives of this research were to address the compositional changes and potential sensory impact of 10 Vitis vinifera red wines during storage for 12 months at 15°C and to evaluate the descriptive sensory and consumer characteristics of the 10 red wines and compare those with compositional components.
Materials and Methods
Wines.
Three Vitis vinifera bulk wines, Cabernet Sauvignon, Merlot, and Zinfandel, were purchased from a local, private winery. The wines were originally produced in California and shipped to a local winery in 2009. The wine (100 L of each variety) was transported from the private winery to the University of Arkansas Wine Research Laboratory in watertight food-grade polyethylene tanks. After transport, wines were transferred into 19-L and 3.8-L glass containers. All containers were flushed with nitrogen prior to filling; nitrogen flushing was used during all experimental phases to reduce the chance of oxidation. Prior to blending, the three wines were screened for potential defects and none were found. There was no indication that the wines underwent malolactic fermentations. Cabernet Sauvignon and Zinfandel were oak-treated during crush.
Wine blending.
To determine the formulas for the blends of Cabernet Sauvignon, Merlot, and Zinfandel varietals, an augmented simplex-centroid mixture design with three components was implemented (Figure 1). The vertices each represent one side of a triangle (ternary plot) and correspond to the single components. The combined percentage of each varietal in each blend totals 100%. The augmented simplex-centroid design allows for an even distribution of blends throughout the plot. The wines were blended volumetrically resulting in 10 treatments: three single-component wines, 100% Cabernet Sauvignon (Cab), 100% Merlot (Mer), and 100% Zinfandel (Zin); three binary blends, 50%Cab+50%Mer, 50%Cab+50%Zin, and 50%Mer+50%Zin; and four tertiary blends, 17%Cab+17%Mer+67%Zin, 17%Cab+67%Mer+17%Zin, 67%Cab+17%Mer+17%Zin, and 33%Cab+33%Mer+33%Zin. This type of mixture design was selected because of the equal product location spacing in the ternary plot and the total number of blends produced. The 10 wines were enough to obtain adequate descriptive and consumer sensory and analytical understanding and were cost-effective from a sensory evaluation standpoint. After blending, wines were bottled into glass containers and sealed with rubber corks. Wines were stored at 15°C until needed for analytical or sensory evaluations. Wines were brought to room temperature (21°C) prior to evaluations.
Compositional and color analysis.
Wine composition and color analysis was performed at 0, 180, and 360 days. Wines were removed from storage and placed at room temperature (21°C) overnight before analysis. To prepare the samples for analysis, the wine samples were inverted 10 times and 45 mL of each wine was poured into centrifuge containers. The wine was sonicated for 5 min and then centrifuged at 13,250 g for 10 min. An additional compositional and color analysis of the 10 wines was performed at 30 days, when sensory analysis was also performed.
Basic wine composition.
Wine pH was measured using a pH meter (model 250; Beckman Coulter, Fullerton, CA) with a three-point calibration (1.68, 4.0, and 7.0). To measure titratable acidity (tartaric acid in g/L), 5 mL of each wine was placed in 125 mL degassed, deionized water. The mixture was titrated with 0.1 M sodium hydroxide to an endpoint of pH 8.2 (Iland et al. 2004). Ethanol levels (%v/v) were measured initially (day 0) using a Dujardin-Salleron ebulliometer (model 360; Paris, France). Free sulfur dioxide (SO2) was measured on the single-component wines using Titrets (CHEMetrics, Calverton, VA).
Color analyses.
Wine color analysis was conducted using a ColorFlex system (HunterLab, Reston, VA) and a Unicam Helios Beta UV-Vis spectrophotometer (Unicam, Cambridge, UK). The ColorFlex system uses a ring and disk set (to control liquid levels and light interactions) for measuring translucent liquids in a 63.5-mm glass sample cup with an opaque cover to determine Commission Internationale de l’Eclairage (CIE) Lab transmission values of L*=100, a*=0, and b*=0 (C.I.E. 1986). The CIELAB system describes color variations as perceived by the human eye. CIELAB is a uniform three-dimensional space defined by colorimetric coordinates, L*, a*, and b*. The vertical axis L* measures lightness from completely opaque (0) to completely transparent (100), while on the hue-circle, +a* red, −a* green, +b* yellow, and −b* blue are measured. Hue angle, calculated as arctan (b*/a*), described color in angles from 0 to 360°: 0° is red, 90° is yellow, 180° is green, 270° is blue, and 360° is red. Chroma, calculated as ((a*)2 + (b*)2)0.5, identified color by which a wine appears to differ from gray of the same lightness and corresponds to intensity of the perceived color. Red color density of the wines was measured spectrophotometrically as red color (520 nm) + yellow/brown color (420 nm) (Iland et al. 2004). Spectrophotometer measurements were standardized to a 1 cm cell.
Nutraceutical components.
Total phenolics were analyzed using the Folin-Ciocalteu assay (Slinkard and Singleton 1977) with a gallic acid standard. Serial dilutions were performed to provide the standard curve formula. Results were expressed as gallic acid equivalents (GAE) using an absorbance of 760 nm. The total anthocyanin content of the wines was determined by a pH differential method with buffer solutions of pH 1.0 (potassium chloride) and pH 4.5 (sodium acetate) (Giusti and Wrolstad 2005). Absorbance was measured at 510 nm and 700 nm A = [(A510−A700)pH1.0 − (A510−A700)pH4.5], with a molar extinction coefficient for malvidin-3-glucoside of 20,200 (Giusti and Wrolstad 2005). Results were expressed as milligrams of malvidin-3-glucoside equivalents per 100 mL of wine. Samples were treated with potassium metabisulfite (KMBS) to analyze polymeric color, and absorbances were read at 420, 510, and 700 nm. KMBS was used to bleach monomeric anthocyanins present in wine, while polymeric anthocyanins remain colored and detectable. Results were expressed as percent polymeric color. Absorbances for all nutraceutical analyses were measured using an 8452A photodiode array spectrophotometer (Hewlett-Packard, Palo Alto, CA).
High-pressure liquid chromatography.
The HPLC and HPLC-MS methodology of Cho et al. (2004) was used to identify and quantify anthocyanins.
Descriptive analysis.
The 10 wines were evaluated by a trained panel (n = 9) at Applied Sensory, LLC (Fairfield, CA) in triplicate. All panelists either worked in the wine industry and/or had professional experience tasting wines. A structured 10-point scale was used to evaluate wine attributes in three categories: appearance (overall color intensity, red color intensity, brown tint, and clarity), aroma (overall aroma intensity, fusel, berry, jam/dried fruit, herbaceous, vegetative, spicy, and caramelized), and flavor by mouth (overall flavor intensity, acidity, berry, spicy, astringency, mouthfeel/body, ethanol burn, and duration of flavors). The terms were selected from a comprehensive list developed by the trained panel. Samples were evaluated over two sessions, with all of replication 1 and half of replication 2 analyzed during the first session and the remaining wines evaluated in the second session. A modified Latin square design was used to randomize the sample presentation across the panelists. The wines (50 mL) were served at 21°C in covered, clear tulip-shaped wineglasses. Panelists expectorated the wines and cleansed their palates with unsalted crackers and bottled water.
Consumer evaluation.
The consumer study was conducted at the Sensory and Consumer Research Center at the University of Arkansas, Fayetteville. A total of 108 red wine consumers were recruited to participate (78 women, 30 men). Panelists had to be acceptors of Cabernet Sauvignon, Merlot, and/or Zinfandel wines (rated 3 on 5-point acceptance scale), consume at least one glass of wine per week, not to exceed half a bottle per day, and at least 21 years old.
A balanced complete block design was implemented, such that each participant evaluated the 10 samples over two separate sessions. The wines were served at 21°C in clear, plastic cups. Panelists expectorated the wines and cleansed their palates with unsalted crackers and bottled water. A warm-up sample was served to every consumer at the beginning of each session to eliminate first-position bias. Consumers received 44 mL of five randomized samples per session. This serving protocol (the division of testing into two sessions) helped to reduce sensory fatigue and limit intake of alcohol. Unsalted crackers and water were consumed between each sample. Participants answered liking questions on a 9-point hedonic scale (1 = dislike extremely, 9 = like extremely) (Peryam and Pilgrim 1957) for overall liking (OL), appearance, aroma, overall flavor, sweetness, dryness, tartness, mouthfeel/body, and persistency. Demographic information (sex, age, and income) was collected from the consumers, but not used in this analysis.
Experimental design.
The 10 wines were stored at 15°C for 12 months. Compositional and color components were measured at days 0, 180, and 360 in triplicate. Compositional and color components were also measured at day 30 for comparison with the descriptive sensory intensity ratings and the consumer sensory ratings. The descriptive panel evaluated the wines in triplicate.
Statistical analysis.
Analyses were conducted using JMP (ver. 8.0; SAS Institute Inc., Cary, NC). Tukey’s HSD was used for mean separation. Descriptive results were analyzed using principal component analysis (PCA) of the correlation matrix using The Unscrambler (ver. 9.2; Camo Process, Oslo, Norway). Pearson’s correlation was used to understand the relationship between descriptive intensity scores and the consumer sensory ratings to instrumentally measured composition. For the consumer evaluation, samples were analyzed for analysis of variance (ANOVA) with treatment (1 to 10) and panelist or consumer (random) as main effects, with mean separation from Fisher’s least significant difference test (α = 0.05).
Results and Discussion
Generally, the blended wine treatments had composition values with patterns similar to the primary wine within the blend (Table 1). Initial (day 0) pH values of the wines were between 3.43 and 3.68, and titratable acidity values were between 5.93 and 7.67 g/L. Alcohol content of 100%Cab, 100%Mer, and 100%Zin was 13.4, 13.4, and 12.3% (v/v), respectively, and free SO2 levels were 70, 70, and 40 mg/L, respectively. The color values (red color density, L*, chroma, and hue) and the nutraceutical values (total anthocyanins and percent polymeric color) of the wines indicated that 100%Cab wine was darker, with more nutraceutical content, followed by the 100%Mer wine and then the 100%Zin wine.
Wine composition and color during storage.
The wines were stored at 15°C, and composition and color analyses were conducted at day 0, 180, and 360. Significant differences were found among the three primary wines for all attributes within each storage time (data not shown). The pH and titratable acidity of the primary wines changed during storage, but consistently remained within an acceptable range of 3.43 to 3.68 and 5.93 to 7.95 g/L, respectively. Red color density of 100%Cab (9.0) and 100%Mer (5.9) wines increased to 10.2 and 7.9, respectively, from 0 to 360 days. Chroma for 100%Cab (6.8), 100%Mer (15.3), and 100% Zin (44.8) wines decreased to 1.5, 7.9, and 39.8, respectively, during storage. L* and hue values of the wines were not greatly affected by storage. Total phenolics, percent polymeric color, and total anthocyanin values of the wines were also affected by storage (Figure 2).
Total phenolics.
Changes in total phenolics were expected to remain relatively stable over time, as transformations affected anthocyanin content and formation of polymeric pigments. These reactions commonly occur in juice and fruit processing with decreases in total anthocyanins and increases in polymeric color from anthocyanin-procyanidin formation, resulting in minor phenolic changes (Howard et al. 2010). This trend was applicable from day 0 to day 180 (Figure 2). However, a reduction in total phenolics for all wines occurred from day 180 to day 360.
In a study of Tempranillo, Graciano, and Cabernet Sauvignon wine blends stored for 23 months, there was a similar vacillating trend of total phenolics (Monagas et al. 2006). Polyphenolic content initially decreased 0 to 9 months, increased steeply from 9 to 18 months, and decreased through the remainder of the study. This variation was attributed to the transformation of phenolic compounds into condensed forms and the different reactivities of those compounds in the Folin-Ciocalteu method, in which certain organic and inorganic substances may interfere with the test accuracy, usually sugars (Prior et al. 2005). The trend of total phenolics in our study was the opposite of Monagas et al. (2006), although both showed significant variation, possibly due to the analysis method.
Percent polymeric color.
During storage, the number of monomeric anthocyanins should decrease as anthocyanins are polymerized with tannins, resulting in an increase in polymeric color (Somers and Evans 1974), which was the case in this study (Figure 2). Zinfandel, the lightest wine, had the greatest increase in polymeric color. Initial (day 0) polymeric color of Zinfandel wine was significantly lower than the other wines, so this increase was expected. Wines with high proportions of Zinfandel did not appear to be affected by the light Zinfandel color, perhaps due to the presence of the other wines in the blend. Cabernet and predominantly Cabernet blends had the highest percent polymeric color. Initially, there was more variability among the wines for polymeric color. However, as storage time progressed, the variability among wines decreased. Storage temperature and duration influence color and pigment degradation, with the most noticeable differences in color occurring in the first year (Dallas and Laureano 1994, Somers and Evans 1986).
Total anthocyanins.
Anthocyanin content is primarily determined by cultivar, but environmental, growing, and processing conditions are also contributors. Studies have shown that total anthocyanin content of wines decreased during storage due to the condensation reactions of monomeric anthocyanins with tannins and the increase in polymeric pigments (Dallas and Laureano 1994, Monagas et al. 2006). With results from this study, we expected to see decreases in anthocyanin content of the wines over the 12-month storage (Figure 2), which was consistent with other research (Villamor et al. 2009) and paralleled the decrease in red color density.
HPLC analysis of wines was determined initially (day 0) and at day 360 to evaluate changes in anthocyanins during storage. Twelve peaks were detected using HPLC-MS, with similar formations among the wines (Table 2). The first six peaks were consistent during storage for 360 days, excluding peak 2 (cyanidin-3-O-glucoside; Cyd-3-glu), which was initially detected in small amounts. Others included delphinidin-3-O-glucoside (Dpd-3-glu), petunidin-3-O-glucoside (Ptd-3-glu), and peonidin-3-O-glucoside (Pnd-3-glu). The primary anthocyanins found in all wines were malvidin-3-O-glucosides (Mvd-3-glu), which was expected as these anthocyanins are predominant in V. vinifera grapes (Baldi et al. 1995). Peaks 6, 7, and 12 were classified as pyranoanthocyanins, which are anthocyanin derivatives, formed through condensation reactions that contribute an orange-red wine color and increase color stability (Morata et al. 2003). Peak 6 was identified as vitisin A, derived from a Mvd-3-glu and pyruvic acid reaction (Bakker et al. 1997, Vivar-Quintana et al. 2002). Vitisin compounds form with anthocyanins during the latter stages of fermentation through a condensation reaction of acetaldehyde and pyruvic acid. Spectral data from peak 12 was consistent with Fulcrand et al. (1996), indicating it forms from the covalent binding of major anthocyanins with 4-vinylphenol. Peaks 8 through 11 were acylated anthocyanins. These occur through the formation of esters at the glucose position and commonly form acetyl, caffeoyl, and p-coumaroyl derivatives.
Principal component analysis (PCA) indicated the changes in peak detection in wines from day 0 to day 360 (Figure 3). PC1 and PC2 accounted for 86% of total variation. Peaks 9 and 10 were excluded from analysis due to nondetection; peak 1 was only detectable at day 0; and peak 12 was only detectable at day 360. The wines clearly divided on PC1, with wines at day 0 on the left of the map and wines at day 360 on the right. Wines at day 0 were related to peaks 1–5, 8, and 11. These are the typical anthocyanins present in V. vinifera. Peaks 8 and 11 were acylated anthocyanins. Wines at day 360 were related to pyranoanthocyanins (peaks 6, 7, and 12), which form during maturation.
Total anthocyanin content of each wine measured by HPLC decreased during storage for 360 days, corroborated by the spectrophotometric measurement of total anthocyanins. Peak 12 was detected at day 360 for Zinfandel and predominantly Zinfandel wines, which may indicate the greater color stability of those wines during storage compared to the other wines.
Descriptive analysis.
Initial intensity scores for the wine descriptive attributes were determined (Table 3). The greatest variation in scores occurred for the color and mouthfeel attributes. Overall, blends were scored similar to their predominant wines. The 100%Mer wine appeared to be highly related to vegetative aroma, which corresponded to its high attribute mean score. Blends with low amounts of Zinfandel wine and high amounts of Cabernet and Merlot wine were characterized by more intense body, aroma, and flavor attributes, which, in this study, indicated that Cabernet Sauvignon and Merlot wines had these desirable intense properties, more so than Zinfandel wine. For Zinfandel wine and primary Zinfandel blends, berry flavor, clarity, and perceived acidity were distinguishing characteristics. The binary blends were located between their respective single wines, indicating that they were characterized by each of the wines. Grape cultivar, fruit ripeness, and/or processing methods impact sensory differences in the wines.
Principal component analysis (PCA) illustrated the relationship between the 10 wines and descriptive-analysis rated attribute intensities (Figure 4). PC1 and PC2 accounted for 89.0% of the total variation (82.0% and 7.0%, respectively). PC1 was characterized by color attributes, specifically red color intensity and depth of color and PC2 by vegetative aroma and astringency. The three single wines (100%Cab, 100%Mer, and 100%Zin) were located outside the central region where the blends and most attributes were located, indicating that the panel was able to differentiate between the samples and score attributes accordingly. The single wines each had distinct characteristics which distanced them from the blends. For example, 100%Zin wine was located far from depth of color and red color intensity, indicating it was a lighter wine. However, other blends high in Zinfandel (50%Cab+50%Zin, 50%Mer+50%Zin, 17%Cab+17%Mer+67%Zin) were located more centrally in the map, indicating the effect of blending this lighter wine with more robust wines, no matter what the proportion.
Descriptive analysis and compositional data comparisons.
Correlations were determined for the analytical composition data and descriptive sensory data of the wines to understand the relationships among the wine components (Table 4). There were high correlations between color and mouthfeel attributes. Mouthfeel/body of the wines was positively correlated (r > 0.90) with red color intensity, depth of color, flavor intensity, red color density, total anthocyanins, and polymeric color. There was a relatively high correlation (r = 0.87) between ethanol burn and mouthfeel/body. The alcohol present in wines (12.6–13.4%) can elicit the impression of burning sensations, but other factors such as wine balance can contribute to ethanol burn. Ethanol burn and total anthocyanins of the wine had a high correlation (r = 0.83), although no correlation was evident between astringency and ethanol burn, similar to results found elsewhere (DeMiglio et al. 2002, Goldner and Zamora 2010).
Red color intensity and depth of color of the wines were highly correlated (r = 0.99). Both attributes were also negatively correlated (r > −0.90) with clarity and chroma and positively correlated (r > 0.85) with flavor intensity, red color density, anthocyanins, and polymeric color. Clarity of wine was negatively correlated (r = −0.94) with mouthfeel/body. Therefore, the more clarity a wine had, the less it was perceived as full bodied.
The presence of phenolic compounds can enhance the red wine color in young wines. However, correlations between total phenolics and red color intensity (r = 0.55) and depth of color (r = 0.59) of the wines were not particularly high. At 30 days of storage at 15°C, there were significant differences in red color density of the wines, most notably between Cabernet (9.60) and Zinfandel (2.84). Zinfandel wine had the lowest red color density, indicating a pale, light-colored wine, and Cabernet Sauvignon wine had higher red color density, indicating a deeper, darker red color.
Astringency was positively correlated with total phenolics (r = 0.80) (Table 4). Astringency is one of the most important sensory characteristics of red wine (Lesschaeve and Noble 2005, Llaudy 2004), so understanding its presence is critical. Within the polyphenolic group of compounds, condensed tannins (proanthocyanidins) elicit tactile sensations of astringent, puckering, or drying (Arnold and Noble 1978, Gawel 1998). Balanced levels of astringency relate to high-quality wine: if the astringency is too low, the wine is considered flat and dull (Gawel 1998). The correlation between astringency and total phenolics in this study was similar to results elsewhere (Goldner and Zamora 2010). Arnold and Noble (1978) also showed that an increase in phenolic content corresponded to an increase in perceived astringency.
Red color intensity of wine was highly correlated with anthocyanin content and red color density (r = 0.94), but not with astringency (r = 0.14) (Table 4). Wine pH was positively correlated (r = 0.68–0.71) with mouthfeel/body, red color intensity, depth of color, and flavor intensity, but negatively correlated (r = −0.76) with clarity. Anthocyanin content was positively correlated with mouthfeel/body, red color intensity, depth of color, and overall flavor intensity and negatively correlated with clarity. Villamor et al. (2009) found low correlations between alcohol burn/bitterness and anthocyanins, polymeric pigments, and tannin content in Cabernet Sauvignon and Merlot wines.
Consumer evaluations.
Means for the 10 wines indicated that 100%Zin wine was liked significantly less for almost every attribute, including overall liking, appearance, overall flavor, sweetness, tartness, mouthfeel, and persistency (Table 5). The differences in red color density of the 10 wines at the time of the consumer evaluation are shown in Table 1. The 100%Cab and 100%Zin wines had the highest and lowest amounts of red color, respectively, with the red color value of the 33%Cab/33%Mer/33%Zin wine centrally located. The difference between 100%Zin wine and the other wines may have been exacerbated when directly compared to more robust, tannic reds, and Zinfandel wine was therefore seen as a weaker wine in these characteristics. When Zinfandel wine was blended with Cabernet Sauvignon and Merlot, liking scores improved significantly, even for blends with still-dominant proportions of Zinfandel, indicating that blending an overall lighter-bodied wine with a full-bodied wine can positively affect consumer acceptance. Liking for the other wines did not exhibit the same behavior as the Zinfandel. They were not significantly different for mouthfeel, persistency, or dryness and remained relatively stable in their trends.
Consumer evaluations and compositional data comparisons.
Correlations were determined for the analytical composition data and consumer evaluation data of the wines (Table 6). Wine pH was not significantly correlated to the consumer attributes. Overall liking of the wines was correlated (r > 0.77) to the other consumer attributes: appearance, aroma, overall flavor, tartness, mouthfeel, and persistency. Liking of appearance of the wines was positively correlated to red color density (r = 0.83), total anthocyanins (r = 0.85), and percent polymeric color (r = 0.93) and negatively correlated to L* (r = −0.99), chroma (r = −0.91), and hue (r = −0.99), emphasizing that color/appearance of the wine is an important characteristic for consumers. Additionally, percent polymeric color of the wines was correlated to the overall liking (r = 0.72), aroma (r = 0.79), overall flavor (r = 0.67), tartness (r = 0.76), mouthfeel (r = 0.81), and persistency (r = 0.71).
Conclusions
The composition and color values of the blends of Cabernet Sauvignon, Merlot, and Zinfandel were similar to their predominant wines. The color values (red color density, L*, chroma, and hue) and the nutraceutical values (total anthocyanins and percent polymeric color) had expected trends during storage. Major correlations were found between the compositional and color data when compared to the sensory or consumer evaluations. Although wine quality, acceptance, and preference are specific to individual consumers, the intensities of certain sensory attributes of the wines were lessened by blending the wines to produce more acceptable wine for the consumers.
Acknowledgments
Acknowledgment: Funding for this study was provided by USDA-NIFA-SCRI.
- Received September 2011.
- Revision received February 2012.
- Accepted February 2012.
- Published online June 2012
- ©2012 by the American Society for Enology and Viticulture