Advertisement
Research Report

Quality Perception of Interspecific Hybrid Red Wine in Relation to Anthocyanin Profile and Color Parameters

Catherine H. Dadmun, Natalia Quijada-Morin, Régis D. Gougeon, Anna Katharine Mansfield
Am J Enol Vitic.  2026  77: 0770022  ; DOI: 10.5344/ajev.2026.25042

Abstract

Background and goals Interspecific hybrid winegrapes are of increasing interest in the context of climate change due to their disease resistance and cold hardiness. Color plays an important role in sensory and quality determination of wine, and hybrid wine color can vary from that of Vitis vinifera. Consumer acceptance of hybrid wine color is assumed to be low, but this question has not been explored. Darker colored wines are expected to have higher consumer liking scores, but hybrid wine color is not expected to be inherently disliked.

Methods and key findings Eight commercial wines from across the United States, including five non-vinifera monovarietal wines and three Pinot noirs (V. vinifera L.) from different regions, were used for consumer sensory analysis. Red wine consumers ranked hedonic liking for multiple color parameters, as well as “expected liking” prior to tasting the wine and “actual liking” following in-mouth evaluation. Chemical analyses of these eight wines and nine additional interspecific hybrid samples included UV-vis spectrophotometry, CIE-L*a*b* colorimetry, total anthocyanin content, and monomeric anthocyanin profile analysis via solid-phase extraction and ultra-high-performance liquid chromatography with diode array detection. Wines with higher L* values (lighter color) and higher b* values (more brown hues) received the lowest liking scores.

Conclusions and significance This work suggests that although color is an important predictor of quality expectation in wine, it is a weak predictor for actual wine liking. The color of wines produced from interspecific hybrid grapes falls within the range of those derived from V. vinifera grape cultivars and production methods, thus alleviating concerns that the former will face consumer rejection for not exhibiting “vinifera-like” color.

Introduction

Interspecific hybrid winegrapes offer great potential for the wine industry to respond to the demands of climate change. These winegrapes are bred by crossing two or more Vitis species to increase disease resistance or cold hardiness and thus are also referred to as “disease resistant” cultivars (Bavaresco 2019, Töpfer and Trapp 2022). Disease resistant cultivars are critical for maintaining consistent grape production in increasingly challenging conditions, including greater interseasonal variation in precipitation and temperature, wider temperature extremes, late spring frosts, fewer frost-free days, and severe winter freezing temperatures (Atucha et al. 2018, Töpfer and Trapp 2022, Trapp and Töpfer 2023). Such challenges limit the reliability of quality production in historic wine regions, as well as in newer viticulture areas previously considered poorly suited for winegrape production (Olmo 1971, Martinson et al. 2016, Atucha et al. 2018, Ilnitskaya et al. 2019, Tassoni et al. 2019, Yin et al. 2019, Wang et al. 2021). The development of interspecific hybrid cultivars has made commercial winegrape growing possible in regions facing greater disease pressure and increasingly severe and unpredictable climates (Camargo and Ritschel 2008, Pedneault and Provost 2016, Bavaresco 2019, Holzapfel et al. 2020). There is also a strong push worldwide to significantly lower pesticide applications in vineyards (Pertot et al. 2017, Fouillet et al. 2022, Trapp and Töpfer 2023), and disease-resistant cultivars fulfill this requirement (Basler and Pfenninger 2003, Pedneault and Provost 2016, Jones and McManus 2017, Pertot et al. 2017, Rutto et al. 2021, Trapp and Töpfer 2023).

The commercial success of interspecific hybrid cultivars hinges on consumer acceptance of the wines they produce (Teissedre 2018, González-Centeno et al. 2019, Martinson and Sacks 2019, Gratl et al. 2021, Duley et al. 2023). Before evaluating a wine’s odor, taste, or mouthfeel, consumers first perceive wine through visual examination; thus, anthocyanin and color profiles are critical parameters in establishing expectations and determining wine quality (Somers and Evans 1974, Cheynier et al. 2006, Charters and Pettigrew 2007, Moreno-Arribas and Polo 2009, Lawless and Heymann 2010, Aleixandre-Tudo et al. 2015). In extension focus groups, winemakers have expressed concern that resistant cultivars produce wine with “non-vinifera-like” color, demonstrating differing intensity, lower extraction, hues that stray from the red-purple of Vitis vinifera wines, or unexpected color evolution over maturation.

Depth (intensity of color) and hue (the shade or tint of the wine) are key parameters in wine color perception. Wines with a greater intensity of color are often rated as higher quality, as light color can be interpreted as grape immaturity or poor winemaking (Somers and Evans 1974, Parpinello et al. 2009, Sáenz-Navajas et al. 2011, Jackson 2017). However, wine color parameters can be largely cultivar-dependent (Jackson 2017). The progression of color over time is also a parameter of wine quality, with a change from red to brick-red or brown considered desirable in traditional wines (Verdú Jover et al. 2004, Henry et al. 2006, Kennedy et al. 2006, Moreno-Arribas and Polo 2009, Sáenz-Navajas et al. 2013, Jackson 2014, 2017).

Color in red wines is due to anthocyanins—phenolic compounds extracted from grape skins during maceration, with pH-dependent color expression (Moreno-Arribas and Polo 2009). Throughout production and aging, these wine pigments undergo numerous reactions that yield more stable compounds and changes in visible color properties (Alcalde-Eon et al. 2006, García-Puente Rivas et al. 2006, Ribéreau-Gayon et al. 2006, Monagas and Bartolomé 2009). Instrumental analysis has shown that hybrid wines have different anthocyanin and overall phenolic profiles than traditional wines, but how these profiles affect consumer perception is unknown (De Rosso et al. 2012, Pedastsaar et al. 2014, Kapusta et al. 2018). Furthermore, these parameters have been assessed for only a fraction of the interspecific hybrid cultivars around the world, each of which has a complex parentage (Reynolds 2015, Teissedre 2018, Dry et al. 2019, Foria et al. 2022). This suggests the potential for widely varying anthocyanin profiles and color parameters (De Rosso et al. 2012, Balik et al. 2013, Cheng et al. 2017, Kapusta et al. 2017).

Perceived color is a complex snapshot of many factors, including concentration and proportion of anthocyanins, concentration of cofactors, and levels of copigmentation and polymeric pigment formation (Monagas et al. 2006, He et al. 2012, Li et al. 2017). In general, V. vinifera grapes almost exclusively contain anthocyanin monoglucosides (although trace levels of diglucosides have been detected), whereas non-vinifera and hybrid grapes can contain varying proportions of both anthocyanin monoglucosides and diglucosides (He et al. 2012, Pedastsaar et al. 2014, Xing et al. 2015, Gratl et al. 2021). Diglucosides are less reactive, less colored at wine pH, and have a greater tendency to brown compared to their monoglucoside counterparts (Robinson et al. 1966, Sims and Morris 1985, Boulton 2001, Alcalde-Eon et al. 2006, He et al. 2012, Manns et al. 2013, Burtch et al. 2017). Some disease-resistant cultivars also contain high proportions of anthocyanins other than malvidin-3-glucoside, the primary anthocyanin found in V. vinifera (Romero et al. 2008, Balik et al. 2013, Manns et al. 2013, Ruocco et al. 2017).

Because interspecific hybrid cultivars promise to reduce pesticide use and maintain consistent vineyard production in an increasingly challenging climate, a better understanding of their chemical composition will help to optimize future wine production efforts. In addition, clarifying consumer acceptance and preference is paramount to understanding potential commercial range of wines produced from these cultivars. This work explores the degree to which unique hybrid wine color profiles affect consumer perception of wine quality, thereby laying the groundwork for improved hybrid wine production and expanded product lines. Previous work suggests that darker wines will be perceived as higher quality prior to tasting, but there is no reason to assume that consumers will reject red wines produced from interspecific hybrid cultivars.

Materials and Methods

Wines

Eight of the 17 commercial wines sourced for analysis were also used for sensory evaluation. Monovarietal wines made from 14 interspecific hybrid winegrape cultivars were sourced from commercial wineries in New York, Ohio, Wisconsin, Tennessee, and Iowa. Pinot noir was sourced from three different representative regions: the Willamette Valley in Oregon (Pinot noir OR), the Finger Lakes region in New York (Pinot noir NY), and Burgundy in France (Pinot noir FR). All wines were 2019, 2020, or 2021 vintages with little-to-no oak exposure. Duplicate 750-mL glass bottles of each wine were purchased for analysis (Table 1). Wine pH was measured upon opening to assess the likelihood of its impact on visible color.

View this table:
Table 1

Commercial interspecific hybrid and Pinot noir wines collected for instrumental analysis and sensory evaluation.

Panelists

Eighty-two panelists between 21- to 62-yr-old (average of 32 yr; 56% between the ages of 21 and 29) were recruited via email from a prescreened list maintained by the Cornell Sensory Evaluation Center (Ithaca, NY) in 2022. All panelists reported consuming red wine at least monthly; 67.5% identified their gender as “woman,” 31.3% as “man,” and 1.3% chose not to respond. Prior to sensory evaluation, panelists were asked to sign a consent form approved by Cornell’s Institutional Review Board and were asked not to smoke, eat, or drink immediately prior to their session. Panelists were scheduled for individual 30-min slots for evaluations between 1000 hr and 1600 hr over the course of 1 wk.

Sensory evaluation

Evaluations were performed in individual testing booths under standard fluorescent lighting; booths were equipped with a keyboard and screen; a napkin; a spit cup; and still, odorless water for palate refreshment. Panelists were presented with 30-mL room-temperature wine aliquots in 200-mL ISO wine glasses covered with plastic petri dishes. All eight samples, labeled with randomized three-digit numbers, were presented monadically in a randomized order following a Williams Latin Square, and a 1-min wait time was enforced between samples. Panelists were asked to confirm sample identity prior to answering questions on a digital questionnaire created and executed with RedJade software (Tragon).

Color attribute assessment

Panelists were first instructed to assess the color attributes of the covered wine without smelling or tasting it. Simultaneously, to ensure standard lighting for color assessment, they were presented with a digital picture of an ISO wine glass containing 60 mL of the same wine taken in a 40-cm LED photo box (Shenzhen PULUZ Technology Limited). Panelists were asked to evaluate overall appearance liking, hue (color shade or tint) liking, and color intensity (strength or depth of color) liking, each on a nine-point hedonic scale.

Expected liking versus actual liking

Panelists were first asked to evaluate how much they expected to like the wine, on a nine-point hedonic scale based on visual assessment alone. They were then instructed to remove the cover, smell and taste the wine, and evaluate their actual liking on a nine-point scale. Finally, panelists rated how well the aromas and flavors matched any expectations they had based on the wine’s color. For this question, panelists chose from a five-point scale: 1 = did not match, 2 = somewhat matched, 3 = moderately matched, 4 = largely matched, 5 = perfectly matched.

Conscious use of visual cues in wine consumption

Following the sensory evaluation, panelists were asked how often they consciously use visual cues to assess red wine, using a five-point scale from “never” to “always.” They were then asked how important hue and color intensity were to the visual assessment when consuming wine. Each of these parameters were ranked on a continuous scale from 0 (“not at all important”) to 100 (“extremely important”). Finally, panelists were asked to list any other visual cues they used in assessing wine.

Instrumental analysis

Spectrophotometry and colorimetry were performed at Cornell Agritech (Geneva, NY). Total anthocyanin content, solid-phase extraction (SPE), and ultra high-performance liquid chromatography with diode array detection (UHPLCDAD) analyses were performed at the Institut Universitaire de la Vigne et du Vin of the Université Bourgogne Europe (Dijon, France).

Spectrophotometry

Duplicate bottles of each wine were analyzed in triplicate with a SpectraMax Plus 384 Absorbance Microplate Reader (Molecular Devices). Following calibration, absorbances at 420, 520, and 620 nm were measured in a Type 1 UV quartz macrocuvette with a 10-mm path length (5UV10) (FireFlySci). All wines were diluted in a 1:5 ratio with deionized water to ensure that absorbances remained below a value of 1. Exceptions were the Petite Pearl and Noiret samples which were diluted in a 1:10 ratio due to the extremely dark color of the wine, and the Pinot noir NY, which was left undiluted due to the extremely light color of the wine.

Hue (h) was calculated using absorbances measured at 420 and 520 nm (Sudraud 1958):

Hue=A420A520Eq. 1

Color intensity (CI) was calculated using absorbances measured at 420, 520, and 620 nm (Glories 1984):

CI=A420+A520+A620Eq. 2

Colorimetry

CIE-L*a*b* color analysis was performed on a HunterLabs Colorimeter UltraScan VIS with EasyMatch QC software. Following calibration, 25 mL of each sample was placed into a Type 93 colorimeter cell with a 10-mm path length (93G10) (FireFlySci). Duplicate bottles of each wine were analyzed in triplicate. Following analysis, color difference (ΔE*) between any two samples (‘1’ and ‘2’ presented as examples in Equation 3), hue angle (h*, measured in degrees), and chroma (C*) were calculated using the following equations (CIE 2004):

ΔE*=(L2*−L1*)2+(a2*−a1*)2+(b2*−b1*)2Eq. 3h*=tan−1(b*a*)Eq. 4C*=a*2+b*2Eq. 5

Total anthocyanin content

Total anthocyanin content was estimated using a method based on prior work (Ribéreau-Gayon and Stonestreet 1965) that quantifies anthocyanins on the basis of two major properties: modification of anthocyanin color is pH dependent, and anthocyanins transform into colorless derivatives under the action of reagents such as sodium bisulfite (Ribéreau-Gayon and Stonestreet 1965).

A solution of 1-mL wine, 1-mL ethanol acidified with 0.1% hydrochloric acid (HCl) (v/v), and 20-mL 2% HCl was prepared. In Tube A, 5 mL of this solution was mixed with 2 mL of deionized water. In Tube B, 5 mL of this solution was mixed with 2 mL of 15% sodium bisulfite and left to react in the dark for 30 min. The absorbances of Tubes A and B were measured at 520 nm using a UV-1800 spectrophotometer (Shimadzu) with a 10-mm path-length cuvette and distilled water as a blank. Anthocyanin quantification as mg/L was calculated using the variation in absorbance (ABS) between the control wine and reacted wine with the formula (ABSA - ABSB) × 875 (Rivas-Gonzalo et al. 1992).

SPE of anthocyanin fraction

Monomeric anthocyanins were extracted in duplicate to concentrate and isolate this fraction from other phenolic compounds (Manns and Mansfield 2012). A Chromabond SPE vacuum manifold (Machery-Nagel) was set up with tube racks, solvent reservoirs, Oasis HLB 3cc 60-mg cartridges (Waters), and stopcocks. All solvents were UHPLC-mass spectrometry grade (Fisher Chemical). To precondition each cartridge, 3 mL of 100% methanol followed by 3 mL of 0.01 N HCl were run through under gravity and discarded. Then, 2 mL of the sample was applied to the cartridge and allowed to drain under gravity to maintain a flow rate below 1 to 2 mL/min. This elution was discarded and 2 mL of 0.01 N HCl was similarly run through under gravity and discarded to elute sugars and organic acids. The SPE manifolds were attached to a vacuum and the cartridges were dried under light vacuum pressure for 5 min at 20 psi.

Monomeric compounds were then eluted with 40 mL of acidified acetonitrile solution (95:5 acetonitrile:0.01 N HCl) using the solvent reservoirs. This elution was run under gravity and collected in large culture tubes. Each sample was evaporated to dryness using a rotary evaporator (Buchi) equipped with a water bath held at 30°C. Once completely dry, 3-mL ethyl acetate was added to each test tube and gently swirled and discarded to remove non-anthocyanin monomeric compounds. This ethyl acetate rinse was repeated three times and samples were dried again using the rotary evaporator and water bath. Once dry, 1-mL 0.01 N HCl was added to dissolve the solid anthocyanin, which was then passed through a 0.2-μm polytetrafluoroethylene filter into an amber UHPLC vial and immediately analyzed. Fractionation was performed twice for each wine sample, resulting in duplicate anthocyanin fractions.

UHPLC-DAD

UHPLC-DAD was used to clarify the composition of the anthocyanin extracts. Duplicate anthocyanin extracts were analyzed using a Waters Acquity UHPLC-DAD system equipped with a Raptor ARC-18 150 mm × 2.1 mm column packed with 1.8-μm particles (Restek). Column temperature was held constant at 35°C and the sample temperature was held at 12°C. All solvents were UHPLC grade (Fisher Chemical). A method was developed to optimize the separation of monoglucoside and diglucoside monomeric anthocyanins, with eluent A composed of 0.28% trifluoroacetic acid and 5% methanol (pH 1.6), and eluent B composed of pure methanol. The flow rate was maintained at 0.3 mL/min for the duration of analysis. The elution gradient was 14.3% B for 1 min, 14.3 to 23% B over 9 min, 23 to 27% B over 8 min, 27 to 36.2% B over 7 min, 36.2 to 50% B over 14 min, 50 to 53% B over 1 min, 53 to 100% B over 2 min, 100% B for 5 min, and 100 to 14.3% B over 4 min, for a total run time of 51 min. This method was not used to quantify total anthocyanin extract due to potential SPE effects; quantification was performed separately as previously described.

Standards of oenin chloride (malvidin-3-glucoside) and malvin chloride (malvidin-3,5-glucoside) were purchased from Extrasynthese. A calibration curve with concentrations from 1 to 500 mg/L was prepared for each standard using methanol diluted in water. Anthocyanin concentrations were determined in malvidin-3-glucoside equivalents for all peaks. A separate set of anthocyanin standards were dissolved in acidified ethanol (0.1% formic acid) (Sigma Aldrich and HoneyWell), diluted with ultrapure water, and analyzed for identification of peaks, including delphinidin-3,5-diglucoside; cyanidin-3,5-diglucoside; pelargonidin-3,5-diglucoside; peonidin-3,5-diglucoside; malvidin-3,5-diglucoside; delphinidin-3-glucoside; cyanidin-3-glucoside; petunidin-3-glucoside; pelargonidin-3-glucoside; peonidin-3-glucoside; and malvidin-3-glucoside (Extrasynthese).

Eleven monomeric anthocyanins were identified through comparison of retention time and DAD spectra to commercial standards. Finally, peaks eluting after malvidin-3-glucoside were grouped together as various “modified anthocyanins”, and likely include acetylated and coumaroylated forms of the anthocyanins, as well as potential pyranoanthocyanins or anthocyanin-tannin adducts (Favretto and Flamini 2000, Manns and Mansfield 2012, Balik et al. 2013). Integrated peaks eluting prior to malvidin-3-glucoside that did not match one of the standards were labeled “unidentified.”

Statistical analysis

Statistical analysis was performed using RStudio software ver. 2022.07.0 (R Core Team 2022). Attributes within the sensory study were compared to each other and to CIE-L*a*b* data to explore correlations between visual attributes and liking. Linear mixed models included random effects of panelist and wines and met all assumptions of a linear model. Analysis of variance (ANOVA) using the Satterthwaite’s degrees of freedom method determined significant relationships between variables at p < 0.05. Post-hoc pairwise comparisons were conducted using estimated marginal means to assign statistical lettering (Figures 1 and 2).

A grouped bar graph compares average overall appearance, hue, and color intensity liking for eight wines on a 0 to 9 hedonic scale. The grouped bar graph compares 3 average liking measures for 8 wines. The vertical axis is labeled Hedonic scale, with 9 indicating most liked, and ranges from 0 to 9 in increments of 1. The horizontal axis is labeled Wines. The legend identifies Overall appearance average, Hue average, and Color intensity average, represented by solid, dotted, and diagonally striped bars, respectively. Pinot noir F R has ratings of approximately 7.4, 7.1, and 7.3, with the letter a above all 3 bars. Saint Croix has ratings of approximately 7.3, 7.1, and 7.3, with a above all 3 bars. Pinot noir O R has ratings of approximately 7.1, 7.2, and 7.0, with a, a, and ab above the bars. Maréchal Foch has ratings of approximately 7.1, 7.0, and 6.9, with a, a, and ab above the bars. Marquette has ratings of approximately 6.4, 6.1, and 6.5, with b above all 3 bars. Pinot noir N Y has ratings of approximately 5.1, 4.9, and 4.7, with c above all 3 bars. Norton has ratings of approximately 4.5, 4.0, and 4.8, with d, d, and c above the bars. Corot noir has ratings of approximately 4.1, 3.9, and 4.4, with d, d, and c above the bars. Vertical error bars extend above and below every bar. All values are approximated.
Figure 1

Mean ratings by sensory panelists (n = 82) for overall appearance liking, hue liking, and color intensity liking of eight commercial red wines. Pinot noir wines were from different regions (Burgundy, France [Pinot noir FR]; Willamette Valley, Oregon [Pinot noir OR]; the Finger Lakes, New York [Pinot noir NY]). Letters denote significant differences between pairwise comparisons from the linear mixed model, including random effects of panelist (p < 0.05).

A grouped bar graph compares average overall appearance, expected, and actual liking ratings for eight wines on a 0 to 9 hedonic scale. The grouped bar graph compares 3 average liking measures for 8 wines. The vertical axis is labeled Hedonic scale, with 9 indicating most liked, and ranges from 0 to 9 in increments of 1. The horizontal axis is labeled Wines. The legend identifies Overall appearance average, Expected average, and Actual average, represented by solid, dotted, and diagonally striped bars, respectively. Pinot noir F R has ratings of approximately 7.3, 7.0, and 5.7, with a, a, and ab above the bars. Saint Croix has ratings of approximately 7.2, 7.0, and 5.0, with a, a, and bc above the bars. Pinot noir O R has ratings of approximately 7.1, 6.9, and 6.1, with a above all 3 bars. Maréchal Foch has ratings of approximately 7.1, 6.8, and 5.0, with a, ab, and bc above the bars. Marquette has ratings of approximately 6.4, 6.2, and 4.4, with b, b, and cd above the bars. Pinot noir N Y has ratings of approximately 5.1, 5.1, and 5.2, with c, c, and bc above the bars. Norton has ratings of approximately 4.5, 4.5, and 4.9, with d, d, and bc above the bars. Corot noir has ratings of approximately 4.1, 4.4, and 3.9, with d above all 3 bars. Vertical error bars extend above and below every bar. All values are approximated.
Figure 2

Mean ratings by sensory panelists (n = 82) for overall appearance liking, expected liking, and actual liking of eight commercial red wines. Pinot noir wines were from different regions (Burgundy, France [Pinot noir FR]; Willamette Valley, Oregon [Pinot noir OR]; the Finger Lakes, New York [Pinot noir NY]). Letters denote significant differences between pairwise comparisons from the linear mixed model, including random effects of panelist (p < 0.05).

Results

Sensory analysis

Panelists’ rankings of hue liking and color intensity liking correlated to overall appearance liking (p < 0.001) and were therefore collapsed into one parameter (Figure 1). Notably, one group of five wines was ranked higher for overall appearance liking and expected liking (range = 6.2 to 7.4), distinctly separate from a second group of three wines (Pinot noir NY, Norton, and Corot noir) that ranked lower for these parameters (range = 4.1 to 5.2) (Figure 2). These wines were grouped based on overlapping standard deviations (SD). The Pinot noir wines were dispersed between the two groups, with Pinot noir OR and Pinot noir FR rated highly, but Pinot noir NY rated lower.

Flavor profile matching, defined as how well aromas and flavors matched expectations based on the wine’s color, largely fell between “somewhat matched” and “moderately matched” (Figure 3). When asked about their use of visual perception, the majority of panelists (71%) responded they “always” or “frequently” used visual cues to assess red wine (Figure 4). Panelists’ ranking of the importance of color intensity and hue varied notably. On a scale of “0 = not at all important” to “100 = very important”, the mean score for color intensity was 71.05 (SD = 22.67) and the mean score for hue was 71.03 (SD = 20.99). Wine pH ranged from 3.4 to 3.9 (data not shown) and no correlation with sensory results or color parameters was observed.

A bar graph shows mean flavor profile matching ratings near 2 to 3 for eight wines, with Pinot noir O R rated highest and Marquette lowest. The bar graph compares the mean Matching scale rating for 8 wines. The vertical axis is labeled Matching scale and ranges from 0 to 5 in increments of 1. The horizontal axis is labeled Wines. Pinot noir F R has a mean rating of approximately 2.8, Saint Croix 2.5, Pinot noir O R 3.0, Maréchal Foch 2.3, Marquette 2.2, Pinot noir N Y 2.6, Norton 2.8, and Corot noir 2.5. Pinot noir O R has the highest mean rating, and Marquette has the lowest. Vertical error bars extend above and below every bar, with broad and substantially overlapping ranges across all 8 wines. All values are approximated.
Figure 3

Mean degree to which flavor profile met sensory panelist expectation from visual assessment, where 1 = did not match; 2 = somewhat matched; 3 = moderately matched; 4 = largely matched; 5 = perfectly matched (n = 82). Pinot noir wines were from different regions (Burgundy, France [Pinot noir FR]; Willamette Valley, Oregon [Pinot noir OR]; the Finger Lakes, New York [Pinot noir NY]).

A pie chart shows that 52 percent of sensory panelists frequently assessed wine visually, followed by sometimes, always, rarely, and never. The pie chart presents the frequency at which sensory panelists consciously used visual cues to assess red wine. Frequently is the largest segment at 52 percent. Sometimes accounts for 24 percent, Always accounts for 19 percent, and Rarely accounts for 5 percent. Never is labeled 0 percent outside the chart.
Figure 4

Frequency at which sensory panelists responded that they consciously used visual cues to assess red wine (five-point scale from "Never" to "Always").

Instrumental analysis

The 17 red wines showed a wide range of hues and color intensities via spectrophotometry (Table 2). The Fredonia, Frontenac, and Regent wines had high color intensity, whereas the Petite Pearl and Noiret wines had extremely high color intensity. The lowest color intensities included Chambourcin and the three wines (Pinot noir NY, Corot noir, and Norton) that ranked lowest for both overall appearance and expected liking.

View this table:
Table 2

Spectrophotometric parameters of interspecific hybrid wines and Pinot noir wines from different regions (Burgundy, France [Pinot noir FR]; the Finger Lakes, New York [Pinot noir NY]; Willamette Valley, Oregon [Pinot noir OR]). Hue and color intensity were calculated using absorbance measurements at 420, 520, and 620 nm.

CIE-L*a*b* results showed that lightness values (L*) ranged from 2.6 to 23.9 among all the wines and from 4.5 to 23.9 among the wines used in the sensory study (Table 3). Corot noir, Pinot noir NY, and Norton displayed the highest b* and h* values, denoting more yellow/brown hues. The three least-liked wines for overall appearance liking and expected liking had a browner hue than the purple-hued wines that were ranked higher, as demonstrated by color swatches generated to aid in visualization (Figure 5).

View this table:
Table 3

CIE-L*a*b* colorimetric analysis of interspecific hybrid wines and Pinot noir wines from different regions (Burgundy, France [Pinot noir FR]; the Finger Lakes, New York [Pinot noir NY]; Willamette Valley, Oregon [Pinot noir OR]). Hue angle and chroma were calculated using a* and b* measurements.

Eight wine swatches are ordered vertically from Corot Noir as least liked to Pinot noir F R as most liked, with a dividing line after Pinot noir N Y. The chart presents 8 wine swatches ordered from least liked at the top to most liked at the bottom. From top to bottom, the labels are Corot noir, Norton, Pinot noir N Y, Marquette, Maréchal Foch, Pinot noir O R, Saint Croix, and Pinot noir F R. A horizontal line separates Pinot noir N Y from Marquette.
Figure 5

Color swatches produced based on CIE-L*a*b* colorimetric measurements to aid in color visualization of eight red wines (ordered from least liked to most liked for overall appearance liking). The yellow line demarcates higher- and lower-ranked groups of wines. Pinot noir wines were from different regions (the Finger Lakes, New York [Pinot noir NY]; Willamette Valley, Oregon [Pinot noir OR]; Burgundy, France [Pinot noir FR]).

The color difference calculation (ΔE*, Equation 3) provides a method to convert the tristimulus color parameters into quantifiable observable differences between wine samples. The most similar wine color pairs were Pinot noir OR-Pinot noir FR (ΔE* = 0.9) and Maréchal Foch-Pinot noir OR (ΔE* = 2.3) (Table 4). Two other pairs, also within the group of higher-rated wines, presented only a slight color difference: Maréchal Foch-Pinot noir FR (ΔE* = 3.0) and Marquette-St. Croix (ΔE* = 3.1). The three wine pairs within the lower-ranked group, despite being grouped together, had noticeably different colors: Norton-Corot noir (ΔE* = 12.3), Norton-Pinot noir NY (ΔE* = 17.3), and Corot noir-Pinot noir NY (ΔE* = 6.3). The higher- and lower-ranked groups showed large color differences, particularly with Corot noir and Pinot noir NY (ΔE* range = 30.6 to 37.9). Marquette was on the lower end of the higher-rated group but showed some of the biggest differences with the color parameters of wines in the lower-rated group, suggesting that color measurements do not directly correspond with hedonic rankings.

View this table:
Table 4

CIE-L*a*b* colorimetric analysis values of color difference (ΔE*; >2.7 is considered perceptible by humans [Martínez et al. 2001]) between pairs of red wines. Gray shading indicates wines in the group liked least by sensory panelists; color coding provides visual clues to the range of color difference values (dark green = <2.7, light green = 2.7 to 5, lightest green = 6 to 10, light pink = 20 to 30, dark pink = >30). Pinot noir wines were from different regions: Burgundy, France (Pinot noir FR); the Finger Lakes, New York (Pinot noir NY); Willamette Valley, Oregon (Pinot noir OR).

The tri-dimensional CIE-L*a*b* parameters can be visualized in a two-dimensional space by using lightness (L*) and hue angle (h*, calculated from a* and b*) as axes (Figure 6). In this space, the higher- and lower-ranked groups of wines can be distinguished; however, Corot noir and Pinot noir NY seem to be outliers and Norton is closer in the space to the rest of the wines. This visualization emphasizes the notable color differences between the three lower-ranked wines. The wines not included in the sensory study largely fall in the space around the higher-ranked group of sensory wines, yet this array of wines shows a wide range of color parameters.

A scatterplot compares hue angle and lightness for 17 red wines, with Pinot noir N Y and Corot noir having the highest lightness values. The scatterplot shows 17 red wines by hue angle and lightness. The horizontal axis is labeled Hue angle equals inverse tangent of b asterisk over a asterisk, and ranges from 0 to 50 in increments of 10. The vertical axis is labeled Lightness and ranges from 0 to 25 in increments of 5. A legend identifies filled markers as Subset of sensory wines, while the remaining wines have open markers. Pinot noir N Y is near a hue angle of 38 and lightness of 24. Corot noir is near 39 and 19. Norton is near 29 and 12. Chambourcin is near 25 and 11, Ives near 21 and 9, Baco noir near 24 and 9, Pinot noir F R near 21 and 9, Maréchal Foch near 19 and 7, Pinot noir O R near 20 and 8, Saint Croix near 13 and 4, and Léon Millot near 19 and 7. Fredonia is near 13 and 5, Marquette near 15 and 4, Regent near 12 and 3, Noiret near 18 and 3, Frontenac near 14 and 2, and Petite Pearl near 16 and 2. An oval encloses Pinot noir N Y, Corot noir, and Norton in the upper right region of the graph. All values are approximated.
Figure 6

CIE-L*a*b* colorimetric parameters of 17 red wines graphed by lightness (L*) and hue angle (h*, Equation 4). Red data points are wines used in the sensory study, and green data points are wines analyzed only instrumentally. The ellipse denotes the lower-ranked group for overall appearance liking and expected liking based on standard deviation. Pinot noir wines were from different regions (Burgundy, France [Pinot noir FR]; Willamette Valley, Oregon [Pinot noir OR]; the Finger Lakes, New York [Pinot noir NY]).

Anthocyanin content ranged from 126 to 1045 mg/L (Table 5), with Ives and Regent presenting extremely high values (927.7 and 1044.8 mg/L, respectively). Regent was a notably dark-colored wine with high (but not the highest) color intensity of those tested. In contrast, spectrophotometric and CIE-L*a*b* values for Ives fell in the middle range of color parameters. Total anthocyanin content was similar among the three Pinot noir wines, with values of 175.2, 212.0, and 224.3 mg/L for Pinot noir OR, FR, and NY, respectively. However, these three wines presented different color parameters and hedonic rankings. The Marquette and Fredonia wines had anthocyanin levels similar to those of the Pinot noir wines (224.6 and 229.0 mg/L, respectively). The only wines with anthocyanin concentrations lower than those of Pinot noir, a cultivar known for its low anthocyanin content, were Baco noir and Corot noir (125.7 and 134.9 mg/L, respectively). Despite these very different anthocyanin concentrations, Baco noir and Ives presented similar colorimetry parameters (ΔE* = 2 .7).

View this table:
Table 5

Anthocyanin concentrations for 17 red wines as determined by using an earlier method (Ribéreau-Gayon and Stonestreet 1965).

Monomeric anthocyanin profiles of the 17 wines were tentatively defined using UHPLC-DAD analysis of the duplicate anthocyanin fractions extracted from each wine (Supplemental Table 1). The anthocyanin profiles are visually demonstrated based on the makeup of monoglucosides, diglucosides, and modified anthocyanins (Figure 7). The small percent of remaining anthocyanins are listed as “% unidentified”.

A 100 percent stacked bar graph compares 4 anthocyanin profile components across 16 wines, with substantial variation among wines. The 100 percent stacked bar graph compares the Total anthocyanin profile for 16 wines. The vertical axis is labeled Total anthocyanin profile, percent, and ranges from 0 to 100 in increments of 10. The horizontal axis is labeled Wine. Each bar contains Monoglucosides, Diglucosides, Modified anthocyanins, and Unidentified anthocyanins. From bottom to top, Maréchal Foch, marked with an asterisk, contains approximately 40 percent monoglucosides, 29 percent diglucosides, 28 percent modified anthocyanins, and 3 percent unidentified anthocyanins. Norton, marked with an asterisk and numbered 2, contains approximately 8 percent, 31 percent, 57 percent, and 4 percent. Marquette, marked with an asterisk and numbered 3, contains approximately 10 percent, 58 percent, 23 percent, and 9 percent. Corot noir, marked with an asterisk and numbered 4, contains approximately 8 percent, 35 percent, 49 percent, and 8 percent. Saint Croix, marked with an asterisk and numbered 5, contains approximately 5 percent, 60 percent, 32 percent, and 3 percent. Pinot noir F R, marked with an asterisk and numbered 6, contains approximately 74 percent monoglucosides, no visible diglucosides, 16 percent modified anthocyanins, and 10 percent unidentified anthocyanins. Pinot noir N Y, marked with an asterisk and numbered 7, contains approximately 83 percent monoglucosides, no visible diglucosides, 13 percent modified anthocyanins, and 4 percent unidentified anthocyanins. Pinot noir O R, marked with an asterisk and numbered 8, contains approximately 68 percent monoglucosides, no visible diglucosides, 24 percent modified anthocyanins, and 8 percent unidentified anthocyanins. Chambourcin, numbered 9, contains approximately 12 percent, 52 percent, 34 percent, and 2 percent. Baco noir, numbered 10, contains approximately 16 percent, 45 percent, 36 percent, and 3 percent. Léon Millot, numbered 11, contains approximately 2 percent, 70 percent, 26 percent, and 2 percent. Frontenac, numbered 12, contains approximately 7 percent, 74 percent, 15 percent, and 4 percent. Petite Pearl, numbered 13, contains approximately 40 percent, 28 percent, 30 percent, and 2 percent. Fredonia, numbered 14, contains approximately 43 percent, 25 percent, 29 percent, and 3 percent. Regent, numbered 15, contains approximately 22 percent, 52 percent, 23 percent, and 3 percent. Ives, numbered 16, contains approximately 3 percent, 39 percent, 57 percent, and 1 percent. All values are approximated.
Figure 7

Distribution of anthocyanin profiles in 17 red wines as percent of the total sum of the integrated area. Asterisks (*) denote wines that were subjected to sensory evaluation. Pinot noir wines were from different regions (Burgundy, France [Pinot noir FR]; the Finger Lakes, New York [Pinot noir NY]; Willamette Valley, Oregon [Pinot noir OR]).

The distribution of monomeric anthocyanins across wine samples showed that all of the selected hybrid cultivars contained some proportion of diglucoside anthocyanins, although the monoglucoside-to-diglucoside ratio varied (Figure 7). Similar to Maréchal Foch, Petite Pearl and Fredonia contain relatively small proportions of diglucosides; in contrast, the monomeric anthocyanin profiles of Léon Millot and Frontenac show a majority of diglucosides. Ives also contains a very small percentage of monoglucoside anthocyanins, but more than 50% of the anthocyanin profile is made up of modified anthocyanins, comparable to the profiles of Norton and Corot noir.

Discussion

Sensory analysis

Panelist assessment of the importance of color intensity or hue during visual evaluation implied that some consumers consciously consider these individual visual attributes, but the similar values reported for hue, color intensity, and overall appearance liking for each wine demonstrated that visual attributes are used holistically rather than through conscious evaluation of discrete color parameters. Overall appearance liking showed a significant relationship with expected liking (p < 0.001, R2 = 0.78), suggesting that the higher a consumer ranks visual characteristics, the more they expect to like the wine. This is corroborated by studies demonstrating that visual product parameters can shape expectation and perception of taste and aroma, as well as that of overall quality perception (Morrot et al. 2001, Delwiche 2004, Acree and Kurtz 2015, Wang and Spence 2019, Spence 2020). If a consumer dislikes the visual characteristics of a wine, their expectations of flavor, and potentially of quality, could be skewed (Shankar et al. 2010). Perceived or expected liking invoked by visual parameters alone could be an example of the halo effect, in which consumers apply positive correlation of unrelated attributes (Lawless and Heymann 2010).

It is possible that a contrast between expectations based on color and actual impressions when smelling and tasting the wine would influence consumer impression of wine quality. A review of basic and applied wine psychology demonstrates that cues—such as visual attributes—that are present early in interaction with a wine can set expectations and potentially generate crossmodal mental imagery concerning the anticipated tastes and aromas, which then interact with further sensory input (Piqueras-Fiszman and Spence 2012, Spence and Deroy 2013, Acree and Kurtz 2015, Nanay 2018, Spence 2020). A recent study clarified that congruence between visual and olfactory parameters does in fact affect consumer and expert response regarding aroma enjoyment, positive emotional response, and purchase intention (Yuan et al. 2024).

To explore this phenomenon in hybrid wine color, the flavor profile matching question was included in the sensory analysis and correlated with the difference between expected liking and actual liking (p < 0.001, R2 = 0.36). This suggests that the degree to which aromas and flavors matched color-based expectations could predict whether consumers changed their initial liking of the wine from visual analysis alone after smelling and tasting it.

Interestingly, when the wines were analyzed individually, flavor profile matching confidently predicted actual liking for five out of the eight wines (p < 0.001) (Table 6). The three wines that did not follow this trend were also the lowest ranked for both overall appearance liking and expected liking, and no relationship was observed between flavor profile matching and actual liking. This suggests that the predictive capability of color-based flavor expectation does not apply to the lower end of the liking scale, potentially due to the role that aroma, gustatory, and mouthfeel characteristics play in consumer acceptance of red wines (Francis and Williamson 2015). It is possible that since this trio rated lowest for liking of visual parameters and expected liking, it was immaterial whether color matched expectations.

View this table:
Table 6

Statistical results comparing flavor profile matching and actual liking of red wines, demonstrating the distinction between lower-ranked wines (gray shaded) and higher-ranked wines.

Despite the relationship between overall appearance and expected liking, findings suggest that there are factors beyond visual perception that are important to consumers’ overall perception of wine quality. This is supported by recent findings that aroma is the most important sensory predictor for consensus in quality decisions (Caissie et al. 2021). The linear mixed model demonstrated that although the relationship between overall appearance liking and actual liking is significant (p < 0.001), it only explains 8% of the variability in the data (R2 = 0.08). Similarly, the relationship between expected liking and actual liking is significant (p < 0.001), but the linear model only explains 7% of the variability in the data (R2 = 0.07). These models demonstrate that there are other variables, including taste and aroma, that determine actual liking.

Sensory in relation to instrumental color parameters

Within the wine industry, both spectrophotometric and colorimetric methods are commonly used to measure wine color (Negueruela et al. 1995, Savino et al. 2007, Aleixandre-Tudo et al. 2017). Well-established routine wine analysis methods include spectrophotometric absorbance measurements at wave-lengths of 420, 520, and 620 nm, as well as calculations using the Gloires and Sudraud methods for color intensity (CI) and hue (h), respectively (Equations 1 and 2) (Sudraud 1958, Glories 1984, Negueruela et al. 1995, Aleixandre-Tudo et al. 2017).

The CIE-L*a*b* color space is becoming an increasingly common wine color measurment because it better encompasses the range of color perception, using the opponent model of human vision to describe three axes: lightness (L*, 0 = white and 100 = black), red-green (a*), and blue-yellow (b*) (CIE 2004, Pathare et al. 2013). The rectangular coordinates L*, a*, and b* can be transformed into the cylindrical coordinates L* (luminosity), h* (hue), and C* (chroma, or color intensity) to better represent the human experience of color (Equations 4 and 5) (Negueruela et al. 1995, CIE 2004, Minolta 2007).

Spectrophotometric and colorimetric methods offer different benefits depending on research or production goals, so both were explored for the purposes of this study (Negueruela et al. 1995, Martínez et al. 2001, Monagas et al. 2006). Luminosity (L*), hue angle (h*), hue (h, A420/A520), and yellow tones (b*) all predicted overall appearance liking and expected liking, but not actual liking (p < 0.05, R2 > 0.15). Red-green tones (a*) showed no relationship to sensory parameters. A number of studies exploring the relationship between chromatic characteristics and wine quality perception have reported that wines with a darker color (lower L*) and more red hues (higher a*) were perceived as higher quality, whereas lighter color (higher L*) and more yellow/brown hues (higher b* and h*) were associated with lower-quality wines (Sáenz-Navajas et al. 2011, 2016). Similarly, the group of three wines with the lowest overall appearance liking and expected liking scores had a lighter color (higher L*) and a more yellow tone (higher b* and h*) (Figure 6). Beyond this general grouping, there was no direct correlation between hedonic ranking and L*, b*, or h* scores, suggesting that no individual color parameter sufficiently links instrumental color and visual perception in wine. For example, Marquette and St. Croix had similarly low L* values (4.47 and 4.65, respectively), but Marquette was ranked lower in all hedonic attributes.

Interestingly, color intensity (C* and CI parameters) showed no significant relationship with sensory parameters in this study (p > 0.05), despite prior work suggesting that wines with a high color intensity had greater color liking or were perceived as higher quality (Somers and Evans 1974, Parpinello et al. 2009, Sáenz-Navajas et al. 2011, Jackson 2017). The fact that no color parameters predicted actual liking offers further evidence that parameters like aroma, taste, and body are better predictors of a consumer’s overall liking of a wine. This is corroborated in another study, which demonstrated that visual cues showed a less significant relationship to global quality perception than other parameters (Sâenz-Navajas et al. 2016).

The visual difference (Figure 5) between the browner hues of the lower-ranked wines and the more purple hues of the higher-ranked wines is explained by the higher b* and h* values (denoting yellow tones) of browner wines, as well as the fact that hue angle (h* and h) and yellow tones (b*) could predict overall appearance liking and expected liking. These chromatic characteristics have been previously confirmed to represent yellow/brown tones in wine (Li et al. 2017). As wine ages, red-purple hues often transition to brick-orange hues due to the formation of copigments, polymeric pigments, and other anthocyanin derivatives (Monagas et al. 2006, He et al. 2012). In one study, it was found that hue angle (h*) is the coordinate that best correlates with wine age (Hernández et al. 2009); this coordination exists but is less important for b* and does not exist for other color coordinates. Contrary to researcher requests, the Norton wine was from 2017, so age could have caused the notably yellow hues; in contrast, however, the Corot noir and Pinot noir NY wines were from 2020 and would not be expected to show age-related browning. The scope of this study did not include measurement of percent polymeric pigment or of concentration of flavanols or xanthylium salts, so these parameters could not be analyzed in relation to wine brownness (Monagas et al. 2006, Li et al. 2017).

It is generally accepted that a 2.7 unit difference (ΔE*, Equation 3) between two samples in the color space is perceptible by humans (Martínez et al. 2001). The higher- and lower-ranked wines group by color parameters and have the largest ΔE* values, with Corot noir and Pinot noir NY particularly far in the color space from the other samples (Figure 6). Lightness (L*) is also a clear separator of the two groups, confirming previous research that darker wines are rated higher by consumers (Sáenz-Navajas et al. 2011, 2016). However, ΔE* values for the pairs of lower-ranked groups of wines demonstrate that although grouped together based on sensory results, these wines still had rather large color differences.

Pinot noir was used in this study not for direct comparison with hybrid cultivars, but to concurrently explore the attributes of a common V. vinifera grape cultivar known to have unique and well-studied color and anthocyanin profiles (Gao et al. 1997, Mazza et al. 1999, Wang and Sporns 1999, Cliff et al. 2007, Dimitrovska et al. 2015, Sparrow et al. 2020). Many factors may contribute to the color difference between the two higher-rated Pinot noirs (FR and OR) and the lower-rated and much lighter-colored Pinot noir (NY). Winemaking style, climatic environment, and viticultural practice could all determine the final color of each wine, despite being made from the same cultivar (Sacchi et al. 2005, Baiano et al. 2016, Harrison 2018). Although wine sourcing for this project aimed to keep certain attributes constant (monovarietal red wines, little-to-no oak exposure, recent harvest), many other winemaking and environmental factors were impossible to control in commercial wines purchased postbottling.

The lack of correlation between panelist liking and the proportion of mono- and diglucoside anthocyanins suggests that diglucoside-dominated anthocyanin profiles can still exhibit wine color desirable by consumers. This may have important implications for an industry hesitant to adopt hybrid grapes and wine. Maréchal Foch, with a high proportion of monoglucoside anthocyanins, was ranked similarly to Marquette and St. Croix, which have large proportions of diglucoside anthocyanins. The two wines most highly ranked for overall appearance liking and expected liking—Pinot noir FR and St. Croix—exhibit very different anthocyanin profiles: St. Croix contains a high proportion of diglucoside anthocyanins and Pinot noir contains none. It is important to note, however, that diglucoside anthocyanins are less pigmented but more stable than their monoglucoside counterparts, which could affect wine color, color evolution, and possibly consumer liking as wines age (Robinson et al. 1966, Hrazdina et al. 1970, Jackson 2008, Burtch et al. 2017).

As expected, the three Pinot noir samples showed roughly the same monomeric anthocyanin profiles, consisting only of monoglucoside anthocyanins and slightly varying ratios of modified anthocyanins. Pinot noir is known to have a very simple anthocyanin profile: unlike many other V. vinifera cultivars, Pinot noir contains very little-to-no acylated (acetylated, coumaroylated, or caffeoylated) anthocyanins (Mazza et al. 1999, Cliff et al. 2007, Sparrow et al. 2020). The small fraction of modified anthocyanins in Pinot noir likely includes anthocyanin-derived pigments such as pyranoanthocyanins, or traces of oligomeric pigments not successfully extracted during SPE preparation (Price et al. 1995, Gao et al. 1997, He et al. 2010).

Norton and Corot noir show very similar percentages of each overall anthocyanin type and are in the same hedonic group, but have a noticeable color difference (ΔE* = 12.3). Their composition of individual monomeric anthocyanins is also very similar, with the total profile including 21 to 23% malvidin-3,5-diglucoside and 56 to 59% modified anthocyanins (Figure 7). The visible difference between the two further suggests that monomeric anthocyanins are only one piece of the puzzle of wine color definition.

Instrumental color profiles

Consumer preference for wines with lower L*, b*, and h* values, both in this study and in prior literature, hints at the possible reception of wines outside the sensory portion of the study. The color intensity of the Chambourcin wine fell within the range of the lower-ranked sensory analysis group, although it was closer to Norton than to the outliers Corot noir and Pinot noir NY. Chambourcin also had L*, b*, and h* values (lightness and yellow/brown hues) higher than any wine outside of this group. Of course, winemaking and viticultural practices, as well as climate and vintage, can play a significant role in determining intensity of wine color; because commercial wines were used, these factors could not be controlled (Sacchi et al. 2005, Baiano et al. 2016, Harrison 2018).

Frontenac, Regent, Noiret, and Petite Pearl wines displayed extremely low L* values (range = 2.6 to 3.2), well below that of Marquette, the darkest wine of the sensory study (L* = 4.5). Given this observed range, it would be interesting to determine whether there is a lower limit to L* correlating with higher quality rankings (Sáenz-Navajas et al. 2011, 2016). It is unknown whether consumer perception of darker products having higher quality is limited to the L* range found in traditional wines or whether it extends further.

Although understanding the anthocyanin fingerprint of each of these hybrid cultivars is critical for further exploration of their color evolution, phenolic interactions, and reactions to winemaking strategies, monomeric anthocyanin profiles cannot solely explain color presentation (Monagas et al. 2006). Color expression in wine is a complex representation of not only the proportion of monomeric anthocyanins, it is also a representation of the resulting products of the many interactions and associations that monomeric anthocyanins undergo in the wine matrix (Monagas et al. 2006, Aleixandre-Tudo et al. 2017). Because acylation increases the stability of the anthocyanin structure, proportions of acylated anthocyanins can also affect color evolution (Escribano-Bailón et al. 2019). Over the course of wine aging, monomeric anthocyanins are lost to condensation reactions that lead to the formation of oligomeric and polymeric pigments, which exhibit brick-orange hues compared to the red-violet hues of young wines (Monagas et al. 2006, He et al. 2012).

The scope of this study did not explore chemical color composition beyond monomeric anthocyanins, but the next steps would be to investigate consumer perception of the other factors implicated in wine color composition, including levels of polymeric pigment, concentrations of cofactors, and copigmentation (Boulton 2001, Boselli et al. 2008). Nevertheless, this pool of data from a wide variety of economically significant interspecific hybrid wines demonstrates the diversity of color, including color intensity and hue, displayed by hybrids. The wide range of anthocyanin concentrations and differing color parameters within this array of wines affirms that the complexity of color parameters in wines is the result of much more than anthocyanin concentration alone (Aleixandre-Tudo et al. 2017, de Freitas et al. 2017).

Conclusion

The hedonic values of eight wines, including five interspecific hybrid cultivars and Pinot noir from three origins, suggests that color is an important predictor for how much a consumer expects to like a wine. Notably, expectations of aromas and flavors invoked by color aligned with differences between expected and actual liking. However, this research reinforces previous findings suggesting that a variety of factors beyond color determine how much a consumer actually likes a wine. Most importantly, consumer perception of color in wines from disease resistant cultivars aligns with that of traditional wine types, as trends observed among V. vinifera wines have been replicated here, such as consumer liking corresponding with low lightness (L*) and low yellow/brown tones (h* and b*) in wines. This suggests that hybrid wine color falls within the color range exhibited by wine styles around the world, further closing the perceived gap between wines made from traditional V. vinifera and from disease resistant grape cultivars.

CRediT Authorship Contributions

CD: Data Curation, Formal Analysis, Investigation; CD, NQM: Methodology; CD, NQM, RG, AKM: Conceptualization; NQM, RGM, AKM: Funding Acquisition, Project Administration, Supervision, Writing - Review & Editing; RG: Software; AKM: Resources

Conflict of Interest

The authors declare no conflicts of interest.

Supplemental Data

The following supplemental materials are available for this article in the Supplemental tab above:

Supplemental Table 1 Quantification of monomeric anthocyanins in commercial interspecific hybrid wines and Pinot noir (Vitis vinifera L.) wines from different regions (Burgundy, France [Pinot noir FR]; the Finger Lakes, New York [Pinot noir NY]; Willamette Valley, Oregon [Pinot noir OR]). Ave RT, average retention time; nd, not determined.

Data Availability

The data underlying this study are available on request from the corresponding author.

Footnotes

  • The authors thank the New York Wine and Grape Foundation (NYWGF), the Région Bourgogne Franche Comté (PINOTYPE projet), and the Université de Bourgogne – Franche Comté through the ISITE-BFC project for financial support, as well as the many wineries that provided samples, with particular thanks to Wollersheim Winery and Vineyards and Liberty Vineyards for sample donation. The authors also thank Alina Stelick at the Cornell Sensory Evaluation Center, the Cornell Statistical Consulting Unit, the DIVVA platform (Université Bourgogne Europe – Institut Agro Dijon) for access to the UPLC-DAD, and Laurence Noret.

  • Dadmun CH, Quijada-Morin N, Gougeon RD and Mansfield AK. 2026. Quality perception of interspecific hybrid red wine in relation to anthocyanin profile and color parameters. Am J Enol Vitic 77:0770022. DOI: 10.5344/ajev.2026.25042

  • By downloading and/or receiving this article, you agree to the Disclaimer of Warranties and Liability. If you do not agree to the Disclaimers, do not download and/or accept this article.

  • Received September 2025.
  • Accepted May 2026.
  • Published online September 2026

This is an open access article distributed under the CC BY 4.0 license.

References

  1. ↵
    1. Acree TE and
    2. Kurtz AJ.
    2015. Painting flavor. In The Chemical Sensory Informatics of Food: Measurement, Analysis, Integration. Guthrie B (eds.), pp. 1-13. American Chemical Society, Washington, DC. DOI: 10.1021/bk-2015-1191
    OpenUrlCrossRef
  2. ↵
    1. Alcalde-Eon C,
    2. Escribano-Bailón MT,
    3. Santos-Buelga C and
    4. Rivas-Gonzalo JC.
    2006. Changes in the detailed pigment composition of red wine during maturity and ageing. Anal Chim Acta 563:238-254. DOI: 10.1016/j.aca.2005.11.028
    OpenUrlCrossRef
  3. ↵
    1. Aleixandre-Tudo JL,
    2. Alvarez I,
    3. García MJ,
    4. Lizama V and
    5. Aleixandre JL.
    2015. Application of multivariate regression methods to predict sensory quality of red wines. Czech J Food Sci 33:217-227. DOI: 10.17221/370/2014-CJFS
    OpenUrlCrossRef
  4. ↵
    1. Aleixandre-Tudo JL,
    2. Buica A,
    3. Nieuwoudt H,
    4. Aleixandre JL and
    5. du Toit W.
    2017. Spectrophotometric analysis of phenolic compounds in grapes and wines. J Agric Food Chem 65:4009-4026. DOI: 10.1021/acs.jafc.7b01724
    OpenUrlCrossRef
  5. ↵
    1. Atucha A,
    2. Hedtcke J and
    3. Workmaster BA.
    2018. Evaluation of cold-climate interspecific hybrid wine grape cultivars for the upper Midwest. J Am Pomol Soc 72:80-93.
    OpenUrlCrossRef
  6. ↵
    1. Baiano A,
    2. Scrocco C,
    3. Sepielli G and
    4. Del Nobile MA.
    2016. Wine processing: A critical review of physical, chemical, and sensory implications of innovative vinification procedures. Crit Rev Food Sci Nutr 56:2391-2407. DOI: 10.1080/10408398.2013.842886
    OpenUrlCrossRef
  7. ↵
    1. Balik J,
    2. Kumšta M and
    3. Rop O.
    2013. Comparison of anthocyanins present in grapes of Vitis vinifera L. varieties and interspecific hybrids grown in the Czech Republic. Chem Pap 67:1285-1292. DOI: 10.2478/s11696-013-0378-9
    OpenUrlCrossRef
  8. ↵
    1. Basler P and
    2. Pfenninger H.
    2003. Disease-resistant cultivars as a solution for organic viticulture. Acta Hortic 603:681-685. DOI: 10.17660/ActaHortic.2003.603.94
    OpenUrlCrossRef
  9. ↵
    1. Bavaresco L.
    2019. Impact of grapevine breeding for disease resistance on the global wine industry. Acta Hortic 1248:7-14. DOI: 10.17660/ActaHortic.2019.1248.2
    OpenUrlCrossRef
  10. ↵
    1. Boselli E,
    2. Giomo A,
    3. Minardi M and
    4. Frega NG.
    2008. Characterization of phenolics in Lacrima di Morro d’Alba wine and role on its sensory attributes. Eur Food Res Technol 227:709-720. DOI: 10.1007/s00217-007-0777-7
    OpenUrlCrossRef
  11. ↵
    1. Boulton R.
    2001. The copigmentation of anthocyanins and its role in the color of red wine: A critical review. Am J Enol Vitic 52:67-87. DOI: 10.5344/ajev.2001.52.2.67
    OpenUrlAbstract/FREE Full Text
  12. ↵
    1. Burtch CE,
    2. Mansfield AK and
    3. Manns DC.
    2017. Reaction kinetics of monomeric anthocyanin conversion to polymeric pigments and their significance to color in interspecific hybrid wines. J Agric Food Chem 65:6379-6386. DOI: 10.1021/acs.jafc.6b05331
    OpenUrlCrossRef
  13. ↵
    1. Caissie AF,
    2. Riquier L,
    3. De Revel G and
    4. Tempere S.
    2021. Representational and sensory cues as drivers of individual differences in expert quality assessment of red wines. Food Qual Prefer 87:104032. DOI: 10.1016/j.foodqual.2020.104032
    OpenUrlCrossRef
  14. ↵
    1. Camargo UA and
    2. Ritschel PS.
    2008. New table and wine grape cultivars: World scenario with emphasis on Brazil. Acta Hortic 785:89-96. DOI: 10.17660/ActaHortic.2008.785.8
    OpenUrlCrossRef
  15. ↵
    1. Charters S and
    2. Pettigrew S.
    2007. The dimensions of wine quality. Food Qual Prefer 18:997-1007. DOI: 10.1016/j.foodqual.2007.04.003
    OpenUrlCrossRef
  16. ↵
    1. Cheng G,
    2. Zhou S-H,
    3. Wen R-D,
    4. Xie T-L,
    5. Huang Y,
    6. Yang Y et al.
    2017. Anthocyanin characteristics of wines in Vitis germplasms cultivated in southern China. Food Sci Technol 38:513-521. DOI: 10.1590/1678-457X.37516
    OpenUrlCrossRef
  17. ↵
    1. Cheynier V,
    2. Dueñas-Paton M,
    3. Salas E,
    4. Maury C,
    5. Souquet J-M,
    6. Sarni-Manchado P et al.
    2006. Structure and properties of wine pigments and tannins. Am J Enol Vitic 57:298-305. DOI: 10.5344/ajev.2006.57.3.298
    OpenUrlAbstract/FREE Full Text
  18. ↵
    CIE. 2004. Colorimetry. 3d ed. Publication 15:2004. Commission Internationale de l'Eclairage Central Bureau, Vienna.
  19. ↵
    1. Cliff MA,
    2. King MC and
    3. Schlosser J.
    2007. Anthocyanin, phenolic composition, colour measurement and sensory analysis of BC commercial red wines. Food Res Int 40:92-100. DOI: 10.1016/j.foodres.2006.08.002
    OpenUrlCrossRef
  20. ↵
    1. de Freitas VAP,
    2. Fernandes A,
    3. Oliveira J,
    4. Teixeira N and
    5. Mateus N.
    2017. A review of the current knowledge of red wine colour. OENO One 51. DOI: 10.20870/oeno-one.2017.51.1.1604
    OpenUrlCrossRef
  21. ↵
    1. De Rosso M,
    2. Tonidandel L,
    3. Larcher R,
    4. Nicolini G,
    5. Ruggeri V,
    6. Dalla Vedova A et al.
    2012. Study of anthocyanic profiles of twenty-one hybrid grape varieties by liquid chromatography and precursor-ion mass spectrometry. Anal Chim Acta 732:120-129. DOI: 10.1016/j.aca.2011.10.045
    OpenUrlCrossRefPubMed
  22. ↵
    1. Delwiche J.
    2004. The impact of perceptual interactions on perceived flavor. Food Qual Prefer 15:137-146. DOI: 10.1016/S0950-3293(03)00041-7
    OpenUrlCrossRef
  23. ↵
    1. Dimitrovska M,
    2. Bocevska M,
    3. Dimitrovski D and
    4. Doneva-Sapceska D.
    2015. Evolution of anthocyanins during vinification of Merlot and Pinot Noir grapes to wines. Acta Aliment 44:259-267. DOI: 10.1556/066.2015.44.0003
    OpenUrlCrossRef
  24. ↵
    1. Dry I,
    2. Riaz S,
    3. Fuchs M,
    4. Sosnowski M and
    5. Thomas M.
    2019. Scion breeding for resistance to biotic stresses. In The Grape Genome. Cantu D and Walker MA (eds.), pp. 319-347. Springer International Publishing, Cham, Switzerland. DOI: 10.1007/978-3-030-18601-2
    OpenUrlCrossRef
  25. ↵
    1. Duley G,
    2. Ceci AT,
    3. Longo E and
    4. Boselli E.
    2023. Oenological potential of wines produced from disease-resistant grape cultivars. Compr Rev Food Sci Food Saf 22:2591-2610. DOI: 10.1111/1541-4337.13155
    OpenUrlCrossRef
  26. ↵
    1. Escribano-Bailón MT,
    2. Rivas-Gonzalo JC and
    3. García-Estévez I.
    2019. Chapter 13 - Wine color evolution and stability. In Red Wine Technology. Morata A (ed.), pp. 195-205. Academic Press, London. DOI: 10.1016/B978-0-12-814399-5.00026-8
    OpenUrlCrossRef
  27. ↵
    1. Favretto D and
    2. Flamini R.
    2000. Application of electrospray ionization mass spectrometry to the study of grape anthocyanins. Am J Enol Vitic 51:55-64. DOI: 10.5344/ajev.2000.51.1.55
    OpenUrlAbstract/FREE Full Text
  28. ↵
    1. Foria S,
    2. Magris G,
    3. Jurman I,
    4. Schwope R,
    5. De Candido M,
    6. De Luca E et al.
    2022. Extent of wild-to-crop interspecific introgression in grapevine (Vitis vinifera) as a consequence of resistance breeding and implications for the crop species definition. Hortic Res 9. DOI: 10.1093/hr/uhab010
    OpenUrlCrossRef
  29. ↵
    1. Fouillet E,
    2. Delière L,
    3. Chartier N,
    4. Munier-Jolain N,
    5. Cortel S,
    6. Rapidel B et al.
    2022. Reducing pesticide use in vineyards. Evidence from the analysis of the French DEPHY network. Eur J Agron 136:126503. DOI: 10.1016/j.eja.2022.126503.
    OpenUrlCrossRef
  30. ↵
    1. Francis IL and
    2. Williamson PO.
    2015. Application of consumer sensory science in research. Aust J Grape Wine Res 21:554-567. DOI: 10.1111/ajgw.12169
    OpenUrlCrossRef
  31. ↵
    1. Gao L,
    2. Girard B,
    3. Mazza G and
    4. Reynolds AG.
    1997. Changes in anthocyanins and color characteristics of Pinot noir wines during different vinification processes. J Agric Food Chem 45:2003-2008. DOI: 10.1021/jf960836e
    OpenUrlCrossRef
  32. ↵
    1. García-Puente Rivas E,
    2. Alcalde-Eon C,
    3. Santos-Buelga C,
    4. Rivas-Gonzalo JC and
    5. Escribano-Bailón MT.
    2006. Behaviour and characterisation of the colour during red wine making and maturation. Anal Chim Acta 563:215-222. DOI: 10.1016/j.aca.2005.11.044
    OpenUrlCrossRef
  33. ↵
    1. Glories Y.
    1984. La couleur des vins rouges, 2e partie: Mesure, origine et interpretation. OENO One 18:253-271. DOI: 10.20870/oeno-one.1984.18.4.1744
    OpenUrlCrossRef
  34. ↵
    1. González-Centeno MR,
    2. Chira K,
    3. Miramont C,
    4. Escudier J-L,
    5. Samson A,
    6. Salmon J-M et al.
    2019. Disease resistant bouquet vine varieties: Assessment of the phenolic, aromatic, and sensory potential of their wines. Biomolecules 9:793. DOI: 10.3390/biom9120793
    OpenUrlCrossRef
  35. ↵
    1. Gratl V,
    2. Sturm S,
    3. Zini E,
    4. Letschka T,
    5. Stefanini M,
    6. Vezzulli S et al.
    2021. Comprehensive polyphenolic profiling in promising resistant grapevine hybrids including 17 novel breeds in northern Italy. J Sci Food Agric 101:2380-2388. DOI: 10.1002/jsfa.10861
    OpenUrlCrossRef
  36. ↵
    1. Harrison R.
    2018. Practical interventions that influence the sensory attributes of red wines related to the phenolic composition of grapes: A review. Int J Food Sci Technol 53:3-18. DOI: 10.1111/ijfs.13480
    OpenUrlCrossRef
  37. ↵
    1. He F,
    2. Mu L,
    3. Yan G-L,
    4. Liang N-N,
    5. Pan Q-H,
    6. Wang J et al.
    2010. Biosynthesis of anthocyanins and their regulation in colored grapes. Molecules 15:9057-9091. DOI: 10.3390/molecules15129057
    OpenUrlCrossRefPubMed
  38. ↵
    1. He F,
    2. Liang N-N,
    3. Mu L,
    4. Pan Q-H,
    5. Wang J,
    6. Reeves MJ et al.
    2012. Anthocyanins and their variation in red wines I. Monomeric anthocyanins and their color expression. Molecules 17:1571-1601. DOI: 10.3390/molecules17021571
    OpenUrlCrossRefPubMed
  39. ↵
    1. Henry P,
    2. Charters S and
    3. Pettigrew S.
    2006. Product involvement and the evaluation of wine quality. Qual Market Res 9:181-193. DOI: 10.1108/13522750610658810
    OpenUrlCrossRef
  40. ↵
    1. Hernández B,
    2. Sáenz C,
    3. De La Hoz JF,
    4. Alberdi C,
    5. Alfonso S and
    6. Diñeiro JM.
    2009. Assessing the color of red wine like a taster’s eye. Color Res Appl 34:153-162. DOI: 10.1002/col.20475
    OpenUrlCrossRef
  41. ↵
    1. Holzapfel BP,
    2. Rossouw G,
    3. Dry I and
    4. Thomas M.
    2020. Productivity and wine assessment of new disease resistant cultivars in Australia. Acta Hortic 1274:79-88. DOI: 10.17660/ActaHortic.2020.1274.9
    OpenUrlCrossRef
  42. ↵
    1. Hrazdina G,
    2. Borzell AJ and
    3. Robinson WB.
    1970. Studies on the stability of the anthocyanidin-3,5-digucosides. Am J Enol Vitic 21:201-204. DOI: 10.5344/ajev.1970.21.4.201
    OpenUrlAbstract/FREE Full Text
  43. ↵
    1. Ilnitskaya E,
    2. Guguchkina T and
    3. Talash A.
    2019. New cold-tolerant grapevine cultivars for red wines. Acta Hortic 1248:95-100. DOI: 10.17660/ActaHortic.2019.1248.14
    OpenUrlCrossRef
  44. ↵
    1. Jackson RS.
    2008. Wine Science: Principles and Applications. 3d ed. Academic Press, London. DOI: 10.1016/B978-0-12-373646-8.X5001-X
    OpenUrlCrossRef
  45. ↵
    1. Jackson RS.
    2014. Wine Science: Principles and Applications. 4th ed. Academic Press, London. DOI: 10.1016/B978-0-12-381468-5.00014-2
    OpenUrlCrossRef
  46. ↵
    1. Jackson RS.
    2017. Wine Tasting: A Professional Handbook. 3d ed. Academic Press, London. DOI: 10.1016/B978-0-12-801813-2.00011-2
    OpenUrlCrossRef
  47. ↵
    1. Jones DS and
    2. McManus PS.
    2017. Susceptibility of cold-climate wine grape cultivars to downy mildew, powdery mildew, and black rot. Plant Dis 101:1077-1085. DOI: 10.1094/PDIS-01-17-0022-RE
    OpenUrlCrossRef
  48. ↵
    1. Kapusta I,
    2. Cebulak T and
    3. Oszmianski J.
    2017. The anthocyanins profile of red grape cultivars growing in south-east Poland (Subcarpathia region). J Food Meas Charact 11:1863-1873. DOI: 10.1007/s11694-017-9568-4
    OpenUrlCrossRef
  49. ↵
    1. Kapusta I,
    2. Cebulak T and
    3. Oszmianski J.
    2018. Characterization of Polish wines produced from the interspecific hybrid grapes grown in south-east Poland. Eur Food Res Technol 244:441-455. DOI: 10.1007/s00217-017-2972-5
    OpenUrlCrossRef
  50. ↵
    1. Kennedy JA,
    2. Saucier C and
    3. Glories Y.
    2006. Grape and wine phenolics: History and perspective. Am J Enol Vitic 57:239-248. DOI: 10.5344/ajev.2006.57.3.239
    OpenUrlAbstract/FREE Full Text
  51. ↵
    Konica Minolta. 2007. Precise Color Communication: Color Control from Perception to Instrumentation. https://www.konicaminolta.com/instruments/knowledge/color/pdf/color_communication.pdf
  52. ↵
    1. Lawless HT and
    2. Heymann H.
    2010. Sensory Evaluation of Food: Principles and Practices. 2d ed. Springer, NY. DOI: 10.1007/978-1-4419-6488-5
    OpenUrlCrossRef
  53. ↵
    1. Li S-Y,
    2. Zhu B-Q,
    3. Li L-J and
    4. Duan C-Q.
    2017. Extensive and objective wine color classification with chromatic database and mathematical models. Int J Food Prop 20:S2647-S2659. DOI: 10.1080/10942912.2017.1381848
    OpenUrlCrossRef
  54. ↵
    1. Manns DC and
    2. Mansfield AK.
    2012. A core-shell column approach to a comprehensive high-performance liquid chromatography phenolic analysis of Vitis vinifera L. and interspecific hybrid grape juices, wines, and other matrices following either solid phase extraction or direct injection. J Chromatogr A 1251:111-121. DOI: 10.1016/j.chroma.2012.06.045
    OpenUrlCrossRefPubMed
  55. ↵
    1. Manns DC,
    2. Coquard Lenerz CTM and
    3. Mansfield AK.
    2013. Impact of processing parameters on the phenolic profile of wines produced from hybrid red grapes Maréchal Foch, Corot noir, and Marquette. J Food Sci 78:C696-C702. DOI: 10.1111/1750-3841.12108
    OpenUrlCrossRefPubMed
  56. ↵
    1. Martínez JA,
    2. Melgosa M,
    3. Pérez MM,
    4. Hita E and
    5. Negueruela AI.
    2001. Note. Visual and instrumental color evaluation in red wines. Food Sci Technol Int 7:439-444. DOI: 10.1106/VFAT-5REN-1WK2-5JGQ
    OpenUrlCrossRef
  57. ↵
    1. Martinson TE and
    2. Sacks GL.
    2019. Flavor challenges of breeding disease-resistant grapes using North American Vitis spp. Appellation Cornell Research Focus 2019-4. Cornell University, Ithaca, NY.
  58. ↵
    1. Martinson TE,
    2. Mansfield AK,
    3. Luby JJ,
    4. Gartner WC,
    5. Dharmadhikari M,
    6. Domoto P et al.
    2016. The Northern Grapes Project: Integrating viticulture, enology, and marketing of new cold-hardy wine grape cultivars in the Midwest and Northeast United States. Acta Hortic 1115:3-12. DOI: 10.17660/ActaHortic.2016.1115.2
    OpenUrlCrossRef
  59. ↵
    1. Mazza G,
    2. Fukumoto L,
    3. Delaquis P,
    4. Girard B and
    5. Ewert B.
    1999. Anthocyanins, phenolics, and color of Cabernet Franc, Merlot, and Pinot noir wines from British Columbia. J Agric Food Chem 47:4009-4017. DOI: 10.1021/jf990449f
    OpenUrlCrossRefPubMed
  60. ↵
    1. Monagas M and
    2. Bartolomé B.
    2009. Anthocyanins and anthocyanin-derived compounds. In Wine Chemistry and Biochemistry. Moreno-Arribas MV and Polo MC (eds.), pp. 439-462. Springer, NY. DOI: 10.1007/978-0-387-74118-5_21
    OpenUrlCrossRef
  61. ↵
    1. Monagas M,
    2. Martín-Álvarez PJ,
    3. Gómez-Cordovés C and
    4. Bartolomé B.
    2006. Time course of the colour of young red wines from Vitis vinifera L. during ageing in bottle. Int J Food Sci Technol 41:892-899. DOI: 10.1111/j.1365-2621.2005.01132.x
    OpenUrlCrossRef
  62. ↵
    1. Moreno-Arribas MV and
    2. Polo MC.
    2009. Wine Chemistry and Biochemistry. Springer-Verlag, NY. DOI: 10.1007/978-0-387-74118-5
    OpenUrlCrossRef
  63. ↵
    1. Morrot G,
    2. Brochet F and
    3. Dubourdieu D.
    2001. The color of odors. Brain Lang 79:309-320. DOI: 10.1006/brln.2001.2493
    OpenUrlCrossRefPubMed
  64. ↵
    1. Nanay B.
    2018. Multimodal mental imagery. Cortex 105:125-134. DOI: 10.1016/j.cortex.2017.07.006
    OpenUrlCrossRefPubMed
  65. ↵
    1. Negueruela AI,
    2. Echávarri JF and
    3. Pérez MM.
    1995. A study of correlation between enological colorimetric indexes and CIE colorimetric parameters in red wines. Am J Enol Vitic 46:353-356. DOI: 10.5344/ajev.1995.46.3.353
    OpenUrlAbstract/FREE Full Text
  66. ↵
    1. Olmo HP.
    1971. Vinifera rotundifolia hybrids as wine grapes. Am J Enol Vitic 22:87-91. DOI: 10.5344/ajev.1971.22.2.87
    OpenUrlAbstract/FREE Full Text
  67. ↵
    1. Parpinello GP,
    2. Versari A,
    3. Chinnici F and
    4. Galassi S.
    2009. Relationship among sensory descriptors, consumer preference and color parameters of Italian Novello red wines. Food Res Int 42:1389-1395. DOI: 10.1016/j.foodres.2009.07.005
    OpenUrlCrossRef
  68. ↵
    1. Pathare PB,
    2. Opara UL and
    3. Al-Said FA-J.
    2013. Colour measurement and analysis in fresh and processed foods: A review. Food Bioprocess Technol 6:36-60. DOI: 10.1007/s11947-012-0867-9
    OpenUrlCrossRef
  69. ↵
    1. Pedastsaar P,
    2. Vaher M,
    3. Helmja K,
    4. Kulp M,
    5. Kaljurand M,
    6. Karp K et al.
    2014. Chemical composition of red wines made from hybrid grape and common grape (Vitis vinifera L.) cultivars. Proc Est Acad Sci 63:444-453. DOI: 10.3176/proc.2014.4.10
    OpenUrlCrossRef
  70. ↵
    1. Pedneault K and
    2. Provost C.
    2016. Fungus resistant grape varieties as a suitable alternative for organic wine production: Benefits, limits, and challenges. Sci Hortic 208:57-77. DOI: 10.1016/j.scienta.2016.03.016
    OpenUrlCrossRef
  71. ↵
    1. Pertot I,
    2. Caffi T,
    3. Rossi V,
    4. Mugnai L,
    5. Hoffmann C,
    6. Grando MS et al.
    2017. A critical review of plant protection tools for reducing pesticide use on grapevine and new perspectives for the implementation of IPM in viticulture. Crop Prot 97:70-84. DOI: 10.1016/j.cropro.2016.11.025
    OpenUrlCrossRef
  72. ↵
    1. Piqueras-Fiszman B and
    2. Spence C.
    2012. The weight of the bottle as a possible extrinsic cue with which to estimate the price (and quality) of the wine? Observed correlations. Food Qual Prefer 25:41-45. DOI: 10.1016/j.foodqual.2012.01.001
    OpenUrlCrossRef
  73. ↵
    1. Price SF,
    2. Breen PJ,
    3. Valladao M and
    4. Watson BT.
    1995. Cluster sun exposure and quercetin in Pinot noir grapes and wine. Am J Enol Vitic 46:187-194. DOI: 10.5344/ajev.1995.46.2.187
    OpenUrlAbstract/FREE Full Text
  74. ↵
    R Core Team. 2022. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org
  75. ↵
    1. Reynolds A.
    2015. Grapevine Breeding Programs for the Wine Industry: Traditional and Molecular Techniques. Woodhead Publishing, Cambridge, UK. DOI: 10.1016/B978-1-78242-075-0.12001-0
    OpenUrlCrossRef
  76. ↵
    1. Ribéreau-Gayon P and
    2. Stonestreet E.
    1965. Le dosage des anthocyanes dans le vin rouge. Bull Soc Chim Fr 9:2649-2652.
    OpenUrlPubMed
  77. ↵
    1. Ribéreau-Gayon P,
    2. Glories Y,
    3. Maujean A and
    4. Dubourdieu D.
    2006. Handbook of Enology Volume 2: The Chemistry of Wine Stabilization and Treatments. 2d ed. John Wiley & Sons, Chichester, West Sussex, UK. DOI: 10.1002/0470010398
    OpenUrlCrossRef
  78. ↵
    1. Rivas-Gonzalo J,
    2. Gutierrez Y,
    3. Hebrero E and
    4. Santos Buelga C.
    1992. Comparisons of methods for the determination of anthocyanins in red wines. Am J Enol Vitic 43:210-214. DOI: 10.5344/ajev.1992.43.2.210
    OpenUrlAbstract/FREE Full Text
  79. ↵
    1. Robinson WB,
    2. Weirs LD,
    3. Bertino JJ and
    4. Mattick LR.
    1966. The relation of anthocyanin composition to color stability of New York State wines. Am J Enol Vitic 17:178-184. DOI: 10.5344/ajev.1966.17.3.178
    OpenUrlAbstract/FREE Full Text
  80. ↵
    1. Romero I,
    2. Sanchez-Ballesta MT,
    3. Escribano MI and
    4. Merodio C.
    2008. Individual anthocyanins and their contribution to total antioxidant capacity in response to low temperature and high CO2 in stored Cardinal table grapes. Postharvest Biol Technol 49:1-9. DOI: 10.1016/j.postharvbio.2007.11.009
    OpenUrlCrossRef
  81. ↵
    1. Ruocco S,
    2. Stefanini M,
    3. Stanstrup J,
    4. Perenzoni D,
    5. Mattivi F and
    6. Vrhovsek U.
    2017. The metabolomic profile of red non-V. vinifera genotypes. Food Res Int 98:10-19. DOI: 10.1016/j.foodres.2017.01.024
    OpenUrlCrossRef
  82. ↵
    1. Rutto LK,
    2. Mersha Z and
    3. Nita M.
    2021. Evaluation of cultivars and spray programs for organic grape production in Virginia. Hort-Technology 31:166-173. DOI: 10.21273/horttech04726-20
    OpenUrlCrossRef
  83. ↵
    1. Sacchi KL,
    2. Bisson LF and
    3. Adams DO.
    2005. A review of the effect of winemaking techniques on phenolic extraction in red wines. Am J Enol Vitic 56:197-206. DOI: 10.5344/ajev.2005.56.3.197
    OpenUrlAbstract/FREE Full Text
  84. ↵
    1. Sáenz-Navajas M-P,
    2. Echavarri F,
    3. Ferreira V and
    4. Fernândez-Zurbano P.
    2011. Pigment composition and color parameters of commercial Spanish red wine samples: Linkage to quality perception. Eur Food Res Technol 232:877-887. DOI: 10.1007/s00217-011-1456-2
    OpenUrlCrossRef
  85. ↵
    1. Sáenz-Navajas M-P,
    2. Campo E,
    3. Sutan A,
    4. Ballester J and
    5. Valentin D.
    2013. Perception of wine quality according to extrinsic cues: The case of Burgundy wine consumers. Food Qual Prefer 27:44-53. DOI: 10.1016/j.foodqual.2012.06.006
    OpenUrlCrossRef
  86. ↵
    1. Sáenz-Navajas M-P,
    2. Avizcuri JM,
    3. Echâvarri JF,
    4. Ferreira V,
    5. Fernandez-Zurbano P and
    6. Valentin D.
    2016. Understanding quality judgements of red wines by experts: Effect of evaluation condition. Food Qual Prefer 48:216-227. DOI: 10.1016/j.foodqual.2015.10.001
    OpenUrlCrossRef
  87. ↵
    1. Savino M,
    2. Limosani P,
    3. Scazzariello R and
    4. Latorraca M.
    2007. Colour investigations into three South Italy red wines: Correlation between sensorially evaluations and instrumentally measured chromatic parameters. Acta Hortic 754:569-574. DOI: 10.17660/ActaHortic.2007.754.76
    OpenUrlCrossRef
  88. ↵
    1. Shankar MU,
    2. Levitan CA and
    3. Spence C.
    2010. Grape expectations: The role of cognitive influences in color-flavor interactions. Conscious Cogn 19:380-390. DOI: 10.1016/j.concog.2009.08.008
    OpenUrlCrossRefPubMed
  89. ↵
    1. Sims CA and
    2. Morris JR.
    1985. A comparison of the color components and color stability of red wine from Noble and Cabernet Sauvignon at various pH levels. Am J Enol Vitic 36:181-184. DOI: 10.5344/ajev.1985.36.3.181
    OpenUrlAbstract/FREE Full Text
  90. ↵
    1. Somers TC and
    2. Evans ME.
    1974. Wine quality: Correlations with colour density and anthocyanin equilibria in a group of young red wines. J Sci Food Agric 25:1369-1379. DOI: 10.1002/jsfa.2740251105.
    OpenUrlCrossRef
  91. ↵
    1. Sparrow AM,
    2. Dambergs RG and
    3. Close DC.
    2020. Grape skins as supplements for color development in Pinot noir wine. Food Res Int 133:108707. DOI: 10.1016/j.foodres.2019.108707
    OpenUrlCrossRef
  92. ↵
    1. Spence C.
    2020. Wine psychology: Basic & applied. Cognit Res Princ Implic 5:22. DOI: 10.1186/s41235-020-00225-6.
    OpenUrlCrossRef
  93. ↵
    1. Spence C and
    2. Deroy O.
    2013. Crossmodal mental imagery. In Multisensory Imagery. Lacey S and Lawson R (eds.), pp. 157-183. Springer, NY. DOI: 10.1007/978-1-4614-5879-1_9
    OpenUrlCrossRef
  94. ↵
    1. Sudraud P.
    1958. Interprétation des courbes d’absorption des vins rouges. Ann Technol Agric 7:203-208.
    OpenUrl
  95. ↵
    1. Tassoni A,
    2. Zappi A,
    3. Melucci D,
    4. Reisch BI and
    5. Davies PJ.
    2019. Seasonal changes in amino acids and phenolic compounds in fruits from hybrid cross populations of American grapes differing in disease resistance. Plant Physiol Biochem 135:182-193. DOI: 10.1016/j.plaphy.2018.11.034
    OpenUrlCrossRef
  96. ↵
    1. Teissedre P-L.
    2018. Composition of grape and wine from resistant vines varieties. OENO One 52:211-217. DOI: 10.20870/oeno-one.2018.52.3.2223
    OpenUrlCrossRef
  97. ↵
    1. Töpfer R and
    2. Trapp O.
    2022. A cool climate perspective on grapevine breeding: Climate change and sustainability are driving forces for changing varieties in a traditional market. Theor Appl Genet 135:3947-3960. DOI: 10.1007/s00122-022-04077-0
    OpenUrlCrossRefPubMed
  98. ↵
    1. Trapp O and
    2. Töpfer R.
    2023. Adoption of new winegrape cultivars to reduce pesticide use in Europe. Am J Enol Vitic 74:0740032. DOI: 10.5344/ajev.2023.23041
    OpenUrlAbstract/FREE Full Text
  99. ↵
    1. Verdu Jover AJ,
    2. Llorens Montes FJ and
    3. Fuentes Fuentes MdM.
    2004. Measuring perceptions of quality in food products: The case of red wine. Food Qual Prefer 15:453-469. DOI: 10.1016/j.foodqual.2003.08.002
    OpenUrlCrossRef
  100. ↵
    1. Wang J and
    2. Sporns P.
    1999. Analysis of anthocyanins in red wine and fruit juice using MALDI-MS. J Agric Food Chem 47:2009-2015. DOI: 10.1021/jf981008j
    OpenUrlCrossRefPubMed
  101. ↵
    1. Wang QJ and
    2. Spence C.
    2019. Drinking through rosé-coloured glasses: Influence of wine colour on the perception of aroma and flavour in wine experts and novices. Food Res Int 126:108678. DOI: 10.1016/j.foodres.2019.108678
    OpenUrlCrossRef
  102. ↵
    1. Wang Z-L,
    2. Xue T-T,
    3. Gao F-F,
    4. Zhang L,
    5. Han X,
    6. Wang Y et al.
    2021. Intraspecific recurrent selection in V. vinifera: An effective method for breeding of high quality, disease-, cold-, and drought -resistant grapes. Euphytica 217:111. DOI: 10.1007/s10681-021-02851-7.
    OpenUrlCrossRef
  103. ↵
    1. Xing R-R,
    2. Li S-Y,
    3. He F,
    4. Yang Z,
    5. Duan C-Q,
    6. Li Z et al.
    2015. Mass spectrometric and enzymatic evidence confirm the existence of anthocyanidin 3,5-O-diglucosides in Cabernet Sauvignon (Vitis vinifera L.) grape berries. J Agric Food Chem 63:3251-3260. DOI: 10.1021/acs.jafc.5b00053
    OpenUrlCrossRef
  104. ↵
    1. Yin L,
    2. Clark MD,
    3. Burkness EC and
    4. Hutchison WD.
    2019. Grape phylloxera (Hemiptera: Phylloxeridae), on cold-hardy hybrid wine grapes (Vitis spp.): A review of pest biology, damage, and management practices. J Integr Pest Manage 10:16. DOI: 10.1093/jipm/pmz011
    OpenUrlCrossRef
  105. ↵
    1. Yuan W,
    2. Guo F,
    3. Li M and
    4. Song H.
    2024. Effects of sensory cues on consumers’ wine taste perceptions and behavior: Evidence from a wine-tasting experiment. Int J Contemp Hosp Manag 36:4171-4191. DOI: 10.1108/IJCHM-08-2023-1160
    OpenUrlCrossRef
Back to top
Print
View full PDF
Email Article
Citation Tools

Open Access
Quality Perception of Interspecific Hybrid Red Wine in Relation to Anthocyanin Profile and Color Parameters
Catherine H. Dadmun, Natalia Quijada-Morin, Régis D. Gougeon, Anna Katharine Mansfield
Am J Enol Vitic.  2026  77: 0770022  ; DOI: 10.5344/ajev.2026.25042
del.icio.us logo Twitter logo Facebook logo Mendeley logo
Save to my folders

Jump to section