VINE AND WINE OPEN ACCESS JOURNAL

Volume 52 > Number 2 > 2018

Sensory analysis of Ontario wines from various water status zones

James J. Willwerth, Andrew G. Reynolds * and Isabelle Lesschaeve

Cool Climate and Institute (CCOVI), Brock University, St. Catharines, ON Canada L2S 3A1

Abstract

Aims: Determinants of the terroir effect in Riesling were sought by choosing vine water status as a major factor. It was hypothesized that consistent water status zones could be identified within vineyards, and, differences in wine sensory attributes could be related to vine water status. Methods and results: To test our hypothesis, 10 Riesling vineyards representative of each Ontario Vintners Quality Alliance (VQA) sub-appellation were selected. Vineyards were delineated using global positioning systems and 75 to 80 sentinel vines were geo-referenced within a sampling grid for data collection. During 2005 to 2007, vine water status measurements [leaf water potential ( ψ)] were collected bi-weekly from a subset of these sentinel vines. Vines were categorized into “low” and “high” leaf ψ zones within each vineyard through use of geospatial maps and replicate wines were made from each zone. Wines from similar leaf ψ zones had comparable sensory properties ascertained through sorting tasks and multidimensional scaling (2005, 2006). Descriptive analysis further indicated that water status affected wine sensory profiles, and attributes differed for wines from discrete leaf ψ zones. Multivariate analyses associated specific sensory attributes with wines of different leaf ψ zones. Several attributes differed between leaf ψ zones within multiple vineyard sites despite different growing seasons. Wines produced from vines with leaf ψ > -1.0 MPa had highest vegetal aromas whereas those with leaf ψ < -1.3 MPa were highest in honey, petrol and tropical fruit flavors. Vines under mild water deficit had highest honey, mineral, and petrol and lowest vegetal aromas. Conclusion: Results indicate that water status has a profound impact on sensory characteristics of Riesling wines and that there may be a quality threshold for optimum water status. Significance and impact of the study: These data suggest that vine water status has a substantial impact on the sensory properties of Riesling wines. Variability of leaf ψ within vineyards can lead to wines that differ in their sensory profiles. These findings were consistent among vineyards across the . These strong relationships between leaf ψ and sensory attributes of Riesling suggest that vine water status is a major basis for the terroir effect. Key words: Vine water status, multivariate statistics, sensory analysis, terroir, precision viticulture

Received : 8 December 2016; Accepted : 5 June 2018; Published : 30 June 2018 DOI: 10.20870/oeno-one.2018.52.2.1669

*Corresponding author : [email protected] OENO One , 2018, 52 , 2, 145-171 - 145 - ©Université de Bordeaux (Bordeaux, France) James J. Willwerth et al.

Introduction (Smart et al. , 1990). Fruit composition may be directly affected by leaf ψ via changes in turgor or Terroir-related studies have been performed with indirectly via the effects of canopy sink competition focus on single variables such as soil (Seguin, 1975) (Smart et al. , 1990) and light penetration in the and climate (Tonietto and Carbonneau, 2004). cluster zone (Smart, 1985). Vines suffering from Climate has been shown to be a very important water stress may have less shoot growth and therefore criterion of the terroir effect as it substantially have better leaf and fruit exposure to sunlight than impacts wine character (Jones and Davis, 2000). Soil, vines growing with abundant water supply with over however, has traditionally been associated as the vigorous canopies and poor fruit exposure. main constituent of terroir. While several have looked at characteristics of soils, including chemical Most research relating to water relations has involved constituents, many have indicated that the soil’s studies with red wine cultivars. This is probably due physical properties involved in regulation of water to the fact that drought stress regularly occurs in the supply to the vine are the most critical with respect to regions of hotter and drier climates where red grapes the terroir effect. In Bordeaux, soil texture and thrive and are widely planted. In many of these cases, rooting depth were most important, and the best soils mild water deficits have been shown to be beneficial were those that were free draining, and those which to red grape and wine quality mainly due to reduced avoided water logging in the rooting zone but limited berry size and increased anthocyanin and tannin water availability later in the season (Seguin, 1970). content (Kennedy et al. , 2002; Roby et al. , 2004). Many soil effects on vine behavior are due to varying Vine water status has substantial impact on vegetative water content levels throughout the growing season performance (Reynolds and Hakimi Rezaei, 2014a,b; and their effects on plant water status. Vine water Smart, 1974), fruit composition (Hakimi and status is the means by which the terroir effect affects Reynolds, 2010; Ledderhof et al. , 2014; Reynolds a wine’s quality and style (Choné et al. , 2001; and Hakimi Rezaei, 2014c; Reynolds et al. , 2007), Koundouras et al. , 1999; Willwerth et al. , 2009). and aroma compounds in white wine cultivars Vine water supply has been noted as a major factor in (Marciniak et al. , 2013; Peyrot des Gachons et al. , the terroir effect due to its impact on early budburst 2005; Reynolds et al. , 2005). In terms of white grape potential and potential vine vigor (Morlat et al. , composition, studies are limited but as in the case of 2001). Grapes cultivated under mild water stress can red grapes mild deficits also seem to improve grape improve berry composition (Matthews and Anderson, composition (Peyrot des Gachons et al. , 2005; van 1988; Smart, 1985) and mild water deficits have been Leeuwen et al. , 2004, 2009). Most studies shown to be a major factor in the terroir effect investigating white wine quality have focused on (Seguin, 1983; van Leeuwen et al. , 2004; 2009). aroma compounds or their precursors. Vine water Leaf water potential ( ψ) has been widely accepted as status had an impact on volatile thiol precursors in a fundamental measurement of plant water status in grapes, and mild water deficits grapevines. Leaf ψ represents a simple and reliable resulted in higher grape quality whereas severe water method for evaluating the physiological condition of deficits limited aroma potential (Peyrot des Gachons a plant, since cell growth, photosynthesis and crop et al. , 2005). productivity are strongly influenced by leaf ψ and its components (Repellin et al. , 1997). The influence of While potential quality has been analyzed through water supply on grapevine development and measuring chemical variables in fruit, there has not physiology has been widely addressed in the been a focus on wine produced from fruit harvested literature (Grimes and Williams, 1990; Hardie and from vines of different water status, nor has attention Considine, 1976; Matthews and Anderson, 1988). been placed upon sensory differences in wines made Grapes are commonly grown in areas with low water solely of vines of different water status. In terms of supply, where many vineyards are not irrigated and white wine production, the authors could not identify vines are subjected to some form of water stress any studies performed to date which used descriptive during the summer months. Generally, the water analysis. Through difference testing, red wines of obtained by grapevines depends on the water table different water status had different sensory and rainfall. The availability of water to grapevines is characteristics (Matthews et al. , 1990). Differences one of the most important factors of terroir and were found in terms of appearance, aroma, flavor, determining wine quality (Deloire et al. , 2005; and taste among irrigation treatments. Through use of Seguin, 1986; van Leeuwen and Seguin, 1994). descriptive analysis, wines Water deficits in vines have been shown to reduce produced from vines of higher water status were shoot growth (Smart, 1974) and canopy density determined to have more vegetal and less fruity

OENO One , 2018, 52 , 2, 145-171 ©Université de Bordeaux (Bordeaux, France) - 146 - aromas and flavors than vines with lower water status (CEC), and base saturation (BS; as % Ca, Mg, and (Chapman et al. , 2005). K) using standard procedures (CSSS, 1993). The intent of our research was to study the impact of 4. Soil water content and vine water status vine water status within vineyard sites through the use of precision viticulture techniques to minimize At each vineyard, soil water content (SWC) and vine any climatic effects. Leaf ψ measurements taken water status measurements were taken bi-weekly throughout the growing season were used in (every 10 to 14 d) from sentinel vines between the combination with GIS software to designate specific end of June and early September (beginning of fruit vines into different leaf ψ categories. Wines were set to pre-harvest). SWC was measured using a then made with fruit harvested from these vines and portable time domain reflectometer (TDR) then chemical and sensory analyses were conducted (Spectrum Technologies, Plainfield, IL). Probe in order to determine differences of these resultant readings were taken in root zones of all sentinel vines wines. at a depth of 20 cm. On the same day, midday leaf ψ was determined on a subset of sentinel vines (≈ 18 Materials and methods vines) using a Scholander-type pressure chamber (Soil Moisture Equipment Corp., Goleta, CA). 1. Site selection Measurements were taken between 1100 and 1400 Ten Riesling vineyard sites were selected throughout under full sun conditions. Leaf ψ readings were taken the Niagara Peninsula, Ontario, Canada. These sites from healthy, undamaged leaves that were sun- were non-irrigated, commercial vineyards and the exposed and at a similar position on the shoot for vineyard blocks had heterogeneous soil types. Each each sentinel vine. A recently fully-expanded leaf site was also representative of each VQA sub- was excised from the sentinel vine with a razor blade appellation. In each vineyard block, a grid-style and immediately placed in a sealable plastic bag. The sampling pattern was established with a “sentinel leaf was placed in the chamber and the pressure was vine” at each grid intersection point. These sentinel increased slowly until air bubbles/sap exuded from vines (72 to 80 per vineyard) were flagged for the leaf petiole. At this point the pressure was identification to be used for data collection recorded. throughout the year. A global positioning system (GPS; GPS900, Leica Geosystems, Scarborough, 5. Viticultural data collection ON) was used to geo-reference each sentinel vine and For each sentinel vine, data were collected annually to delineate the shape and size of each vineyard at vine dormancy for weight of cane prunings as an block. estimate of vine vigor (“vine size”). Yield 2. Geographic information systems (GIS) components (yield per vine; clusters per vine; cluster weight; berries per cluster; berry weight) were either The delineated vineyards and data layers were measured directly or calculated from measured incorporated into a MapInfo Professional 8.0 GIS variables during harvest each season. Fruit was database with Vertical Mapper 3.1 (Northwood sorted based on treatments and retained for GeoScience, Ottawa, ON). Interpolation maps were . Clusters were counted from each generated for all soil and viticulture variables using sentinel vine and samples of 100 berries were taken the inverse distance weighting interpolation to for determination of berry weight and standard fruit cartographically depict the spatial distribution of each composition indices [total soluble solids (TSS; Brix); variable within each vineyard. titratable acidity (TA); pH], whereas samples of 250 berries were taken for monoterpene concentration 3. Soil sampling and composition analyses. Soil samples were collected from every fourth 6. Berry and must composition sentinel vine (seven to 35 vines per ha) with an auger from within the row, 40 to 50 cm away from the Each 100-berry and 250-berry sample was weighed trunk. Soil was taken from a 0 to 45 cm depth and in to determine the mean berry weight. The frozen berry total ≈ 350 g of a homogenized sample was taken. samples were then heated in 250-mL beakers to an Depending on the area of each vineyard block, 15 to internal temperature of 80°C in an Isotemp 228 water 20 soil samples were taken. Soil samples were bath (Fisher Scientific, Ottawa, ON) to dissolve any analyzed for pH, organic matter (OM), phosphorus precipitated tartaric acid. The heated berry samples (P), potassium (K), magnesium (Mg), calcium (Ca), were then cooled, juiced in a laboratory juicer texture (% sand, silt, clay), cation exchange capacity (Omega Products Inc., Harrisburg, PA), and ≈ 35-mL

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was clarified using an IEC Centra CL2 Centrifuge 100 mg N/L was added using a diammonium (International Equipment Co., Needham Heights, phosphate addition. The juice was brought to room MA) to remove large particles. TSS were measured temperature (20°C) and inoculated with on the unclarified berry juice samples using a Saccharomyces cerevisiae strain W15 (Lallemand temperature-compensated Abbé bench refractometer Inc., Montreal, QB) at a dosage of 250 mg/L (American Optical Corp., Buffalo, NY). The pH was following the manufacturer’s recommended measured using an Accumet pH/ion meter Model rehydration procedure. After 12 hr, the carboys were AR50 (Fisher Scientific, Ottawa, ON). TA was transferred to a temperature controlled fermentation measured on 5-mL clarified samples using a Man- chamber set to 16°C and remained there until the Tech PC-Titrate autotitrator (Man-Tech Associates completion of fermentation (to dryness). Wines were Inc., Guelph, ON). Samples were titrated to a pH 8.2 then removed, racked into clean carboys, sulfited to endpoint with 0.1N NaOH solution. Results were 50 mg/L and transferred to -2°C to undergo cold expressed as tartaric acid equivalents (g/L). stabilization and lees contact for ≈ 2 months. Wines Monoterpenes were analyzed for the 250-berry were then racked after warming to room temperature samples using the method developed by Dimitriadis and 250-mL wine samples were taken and frozen at - and Williams (1984) as modified by Reynolds and 25°C for further chemical analysis (TA, pH, Wardle (1989). The free volatile terpene (FVT) and monoterpenes and ethanol; q.v. description of potentially-volatile terpene (PVT) concentrations analysis). The wines were analyzed for residual were expressed as mg/kg. Brix, TA, pH and (reducing) sugar using the Lane-Eynon method and monoterpenes were performed on must samples sucrose was added to the desired level of 15 g/L using methods identical to those for berries. residual sugar. Potassium sorbate (150 mg/L) and SO 2 (30 mg/L) were added immediately prior to 7. Winemaking filtering and bottling. Wines were filtered to 0.45 μm and bottled at 20°C. Wines were stored in at 15°C Within each vineyard block, sentinel vines were and 70% relative humidity in a controlled cellar at sorted based upon leaf ψ and identified on GIS- CCOVI for 12 months prior to sensory analyses. generated maps. Two leaf ψ categories were established as “high” and “low” and wines were 8. Wine composition made with the fruit from vines with the highest and lowest leaf ψ, respectively. There were three Wine TA and pH were determined by methods replicates of both categories (two leaf ψ categories identical to those used for berries and musts. Ethanol treatments x three replicates). These replicates were was determined using a gas chromatography-flame both winemaking and vineyard replicates. Vines from ionization detector (Agilent 6890, Mississauga, ON) intermediate leaf ψ zones were placed into a separate equipped with a Carbowax (30 m × 0.23 mm × 0.25 treatment to represent the appropriate VQA sub- µm) column. A 0 .5-µL wine sample was injected into appellation for that particular vineyard. Winemaking the injection port heated to 250 °C. The carrier gas practices were consistent for all treatments and was helium with a column head pressure of 137.9 replicates to minimize enological effects. kPa. The flow rate of helium carrier gas was 1 .8 mL/min. The oven temperature was programmed to The grapes from each treatment replicate were start at 60 °C, increase to 125 °C at 6 °C/ min, and transported from the vineyard and immediately then increase to 225 °C at 25 °C/min and hold for 1 crushed and de-stemmed at the CCOVI winery using min. The detector temperature was 250 °C and 2 % an electric crusher/destemmer (Model Pillan N1, 1-butyl alcohol was used as an internal standard. A Enoitalia, San Miniato, Italy) into 20-L food-grade six-point calibration curve (4, 6, 8, 10, 12, 14% v/v plastic pails. Sulfur dioxide (SO 2; 30 mg/L) and ethanol) was used and these samples were diluted Scottzyme Cinn-Free pectinase (Scott Laboratories; 1:10. Pickering, ON; 15 µL/L) were added. Crushed grapes were given 2 hr of skin contact at 7°C prior to 9. Sensory analysis pressing. The grapes were pressed to a maximum 0.2 9.1 Sorting tasks MPa using a water bladder press (Model LDC5518, Enoitalia, San Miniato, Italy). Must samples (≈ 250 A group of untrained participants consisting of 16 mL) were taken from each pressing and frozen at - wine professionals from the Niagara wine industry 25°C for further analysis for Brix, TA, pH and were selected on the basis of having extensive monoterpenes (q.v. description of analysis). The experience with Riesling wines. Each person sorted pressed juice was cold settled at 7°C for 24 hr prior the wines into groups based on retro-nasal to being racked into 11-L glass carboys, after which perceptions. Evaluations were conducted in a

OENO One , 2018, 52 , 2, 145-171 ©Université de Bordeaux (Bordeaux, France) - 148 - controlled environment. Sorting was performed on a 9.2 Descriptive analysis per vineyard basis where panelists received the entire The sensory panel ran from October 2007 until set of wines for each vineyard. The presentation of March 2008. Participants consisted of graduate samples was presented according to a Latin Square. students, undergraduate students, staff and faculty Each participant was asked to sort wines into groups who were experienced with Riesling wines and based on similar taste characteristics. No criterion sensory methodology. Most judges had previously was provided to perform this task and the panelists participated in descriptive analysis studies. In the first were free to make as many groups of wines as they year the panel consisted of five males and six wanted but could only group a wine once (i.e. a wine females between the ages of 23 and 65 years (mean = could not appear in more than one group). After 38). In the second year the panel consisted of seven males and three females between the ages of 23 and performing their sorting task, they were asked to 54 years, six of whom were on the previous year’s provide descriptors on the basis of their decision to panel. designate the wines into their respective grouping(s). Data from the sorting tasks were put into a distance Training consisted of 16 sessions conducted over 24 matrix by the frequency that the wines were grouped hr in the first year and 18 hr in second year. In the first session, the panelists were screened for possible together. For each assessor, a value of 1 indicated that anosmias of typical wine aromas. Panelists were also the wines were grouped together and conversely a assessed on odor and taste recognition and ranking value of 0 meant the wines were not grouped ability thorough a series of identification and ranking together. For each vineyard, these matrices were then exercises using model solutions. For the initial converted into similarity/dissimilarity matrices and training sessions, judges generated descriptive terms were analyzed with multidimensional scaling (MDS) describing differences perceived between the wines through XLSTAT-pro v. 2008.5.1 (Addinsoft; Paris, from the study. During the training period, aroma reference standards were given to define the France). MDS was conducted for each task using aroma/flavor descriptors and were discussed and Euclidean distance and was depicted in three- modified based on panel consensus. Standards for dimensional space using XLSTAT-3D plot. In these sweetness, sourness, bitterness, and astringency were maps, the proximity between the wines reflects their also given during training. Panelists rated the similarity. intensities of each attribute for every wine and then

Table 1. Aroma/flavor and taste/mouthfeel attributes used in sensory evaluation of Riesling wines, Niagara Peninsula, Ontario, 2005 to 2006.

Attribute Reference (base wine was a neutral Ontario Riesling wine) Apple/pear 5 g fresh Bartlett pear and 5 g Granny Smith and Red Delicious apples in 50 mL base wine Baking spices (aroma only) 5 mL of stock solution a Fresh lime juice (20 mL), lemon juice (10 mL), and grapefruit juice (5 mL) in 100 mL base Citrus wine Honey President’s Choice alfalfa honey (10 g in 50 mL base wine) Mineral/flint Ground slate (5 g) in 20 mL base wine Peach Yoga peach nectar (25 mL) in 100 mL base wine Petrol 1 drop WD-40™ in 900 mL base wine McCain Tropical fruit juice (10 mL), Rubicon Passion fruit juice (5 mL), and Rubicon Tropical fruit Guava juice (5 mL) in 300 mL base wine Vegetal 10 mL of vegetal stock solution b and canned bean brine (5 mL) in 100 mL base wine Taste/mouthfeel attributes Attribute Reference (in 1000 mL water) Sweet 10 g sucrose Sour 1.5 g citric acid Bitter 0.01 g quinine Astringent 0.05 g aluminum sulfate

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following discussion and panel consensus the most standards at the beginning of each session and the appropriate descriptors to define and discriminate intensity of each aroma, flavor and taste/mouthfeel sensory differences in the wines were chosen. attribute was scored on an unstructured 15-point intensity scale anchored with ‘absent’ and ‘high’. The 17 aroma and flavor attributes that were selected are listed with the composition of the reference Mineral water and filtered water were provided for standard included (Table 1). The four taste and tactile rinsing between samples as well as unsalted crackers. terms selected are also listed with their reference All samples were expectorated following evaluation. standard compositions. These references were 10. Data analysis presented during all formal data collection sessions. Sensory evaluations were conducted using Analysis of variance (ANOVA) was performed with Compusense Five ® version 4.6 software (Guelph, judges, wines, and replicates as fixed effects using ON, Canada) in individual booths at 18EC in the SENPAQ v. 4.1 statistical packages (Qi Statistic Ltd.; sensory laboratory at CCOVI. Wines were served in Reading, UK). Least significant differences (LSD) a random order using a Williams Latin Square design between sample means were used. Principal (MacFie et al. , 1989) and duplicated. Six wines were components analysis (PCA) was performed for the presented during each session as 30-mL samples contained in coded black glasses covered with plastic means of all significant sensory attributes based on a Petri dishes. There were forced 3-min breaks covariance matrix using XLSTAT-pro 2008.5.1 between samples with a 30-min break after the third (Addinsoft; Paris, France) with MX and PLS sample. Judges were familiarized with the reference modules.

Climate data for the 2005 and 2006 growing seasons. Vineland Station, Ontario. Figure 1. Climate data for the 2005 and 2006 growing seasons. Vineland Station, Ontario. T

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Results those obtained from other sensory descriptive methods such as free profiling (Tang and Heymann, 1. General comments and spatial aspects 2002). Similar findings were obtained with The 2005 and 2006 growing seasons were ideal for descriptive and sorting tasks which is consistent with studying the impact of vine water status. Climate data other research experiments (Preston et al. , 2008). for the two growing seasons are shown in (Figure 1). Sorting tasks performed on wines from the 2005 and The 2005 was a hot and dry growing season 2006 indicated that wines of similar leaf ψ with abundant sunshine allowing for an earlier had similar sensory properties through sorting tasks harvest. There were many days throughout June, July and multidimensional scaling. Three dimensions and August which exceeded 30°C. From the were used as opposed to two dimensional space to beginning of May until the middle of August there reduce Kruskal Stress values to acceptable levels < were few rain events > 10 mm of precipitation. 0.1. Then, 3-D plots were created to ease Autumn rainfall events were quite sporadic; however, interpretation rather than using biplots, which did not Riesling harvests occurred prior to many of the capture the necessary space of the data set. For many heavier rains caused by Hurricane Katrina (Figure of the vineyards, the wine treatments were visibly 1A). The 2006 growing season was cooler and wetter separated into two groups based on leaf ψ (date not than the previous vintage. In general, the growing shown). Kruskal Stress values were in all cases season was warm but there were more rainfall events between 0.05 and 0.10 for all vineyards in both particularly during the harvest period. There were, seasons. In some cases one of the treatment replicates however, periods lacking rain. From the beginning of was separate from the other treatment replicates. June until the beginning of August there was an These findings are explainable by the fact that there extensive period of drought with only one rainfall were not large enough differences in leaf ψ between event > 15 mm. June and July were characterized by treatments. The separation of “low” and “high” leaf many days between 25 and 30 °C (Figure 1B). ψ wines was greater in the warmer and drier 2005 Through the use of GPS and GIS technologies, leaf ψ vintage than the cooler, wetter 2006 vintage. The data collected from each vintage were depicted longer period of drought in 2005 resulted in a greater cartographically and analyzed (Figure 2). Consistent range of leaf ψ values within many of the vineyard leaf ψ zones could be delineated and spatial patterns blocks as opposed to the 2006 growing season where of vine leaf ψ were temporally stable within all wetter conditions resulted in less variability within vineyards in the study despite different weather sites. conditions during each growing season. In every 3. Sensory descriptive analysis vineyard studied, distinct regions were delineated that could be categorized as “high” and “low” leaf ψ. This 3.1 Analysis of variance is consistent with Acevedo-Opazo et al. (2008) who found it possible to assess spatial variability of vine Descriptive analysis further indicated that leaf ψ had water status within vineyards, even those small in an effect on wine sensory profiles and also described size (< 1 ha). In many cases, particularly in the hot these differences in terms of aroma and flavor and dry 2005 vintage, the “low” leaf ψ regions attributes, and revealed that wines of different leaf ψ consisted of vines suffering moderate water stress. In zones had distinctive sensory profiles due to general, there were more sites with higher within-site differences in the levels of the intensity of aromas variability in terms of leaf ψ in 2005. and flavors. 2. Sensory sorting tasks 2005: Results from ANOVA are in Table 2. Within- site sensory differences of wines ranged from four to The use of sorting tasks is a simple but very reliable nine aroma and flavor attributes with a median of method to collect similarity data and have been used seven. The most common aroma attributes to differ in numerous studies involving wine (Parr et al. , were honey (four sites), mineral/flint (three sites), 2007). One of its main advantages is that sorting and vegetal aromas (three sites) followed by petrol, tasks are less tedious and time consuming than other tropical fruit and baking spice. Honey (six sites), methods so information can be obtained about apple/pear (five sites), citrus (four sites), and petrol sensory differences among products in an efficient flavors (three sites) were the most common flavor manner (Abdi et al. , 2007). Sorting tasks are attribute differences followed by tropical fruit. Sour particularly suitable when there are a large number of and sweet tastes were different in seven and four samples as was the case in this study. Most vineyards, respectively. Peach aroma and bitter taste importantly results obtained are comparable with were non-factors in 2005 whereas apple/pear aroma,

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Figur e 2. Identification an d sor ting of Ri esli ng sentinel vines for seven O ntar io viney ards, 2005 (A,C,E,G,I,K ,M) and 2006 (B,D, F,H, J,L,N) based on vine wa ter stat us z ones on GIS -gener ated m aps . A,B: Glenlake V ineyar d, Niagar a-on-the-La ke; C,D: Chateau des Ch arme s Vineyard , St . Davids; E ,F: Paragon Vi neyard, J ordan ; G,H: Henry of Pelham Vineyard, St. Catharines; I,J: Myers Vineyard, Vineland; K,L: Flat Rock Vineyard, Jordan; M,N:

Cave Spring Vineyard, Beamsville. Vines in blue/green zones were placed in high water status treatments; vines in o range/ red zon es were placed in lo w water statu s treatm en ts.

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zones (< -1.2 MPa) displayed enhanced apple/pear, citrus, honey, mineral, petrol, and t zones from two sites displayed higher citrus and apple/pear flavors, respectively, while h zones from two other sites showed higher honey flavor. Low leaf wines also commonly h (> -1.0 MPa) counterparts

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OENO One , 2018, 52 , 2, 145-171 ©Université de Bordeaux (Bordeaux, France) - 154 - citrus aroma, mineral/flint flavor, peach flavor and particular criteria. Furthermore, the wines were both astringency were different in one vineyard site only. viticulture and enological replicates and there was no In most circumstances, wines from low leaf ψ zones amalgamation during winemaking, so the fruit/wine’s (< -1.2 MPa) displayed enhanced apple/pear, citrus, geographic location could be accounted for within honey, mineral, petrol, and tropical fruit aromas and the vineyard site. flavors, and reduced intensity of vegetal aromas and flavors. Wines from high leaf ψ zones from two sites ANOVA 2005: Collectively across sites, wines made displayed higher citrus and apple/pear flavors, from vines with the highest leaf ψ (> -1.0 MPa) had respectively, while high leaf ψ zones from two other lowest intensity ratings for apple/pear, honey, sites showed higher honey flavor. Low leaf ψ wines mineral, and tropical fruit aromas and flavors but had also commonly had higher sweetness and lower the highest vegetal aromas and flavors as well as sourness compared to their high leaf ψ (> -1.0 MPa) sourness (Table 2). Conversely, wines produced from counterparts vines of the lowest leaf ψ category (< -1.3 MPa) were highest in honey, tropical fruit and petrol aromas and 2006: Results from ANOVA are in Table 3. flavors and lowest in vegetal aromas and flavors. Differences of sensory attributes within vineyards Wines made from fruit produced under mild water ranged from two to eight aroma and flavor attributes deficit (-1.0 to -1.2 MPa) had sensory profiles highest with a median of four. The most common aroma in mineral/flint, petrol, and honey aromas and attribute difference within vineyards was vegetal apple/pear flavor (data not shown). There were few aroma followed by apple/pear, baking spice, differences in terms of baking spice aroma, mineral mineral/flint and petrol aromas (two sites each). In and peach flavors as well as astringency and terms of flavor attributes, tropical fruit and vegetal bitterness. flavors were the most common descriptors to differ within vineyards (four sites each), followed by ANOVA 2006: Since the 2006 vintage was cooler apple/pear (three sites), honey (three sites), and peach and wetter than 2005, there were less vineyards with flavors (two sites). Sweet taste was different within vines experiencing moderate to high water deficits six vineyards while bitter and sour tastes were (Table 3). Wines produced from vines > -1.0 MPa different in two vineyards. Citrus, honey, peach and had sensory profiles with the lowest apple/pear tropical aromas were different within one vineyard aroma/flavor, baking spice aroma, honey and tropical only. Petrol flavor was a non-factor whereas citrus, fruit flavors, and highest vegetal aromas and flavors, mineral/flint flavors and astringency were site as well as sourness and bitterness. Wines made from specific and only differed within one site. As in 2005, vines that experienced mild deficit (-1.0 to -1.2 MPa) wines from low leaf (> -1.0 MPa) zones displayed had the highest apple/pear, tropical fruit, mineral/flint enhanced apple/pear, citrus, honey, mineral, petrol, aromas and flavors, and lowest vegetal aroma/flavor. and tropical fruit aromas and flavors, and reduced There were no large differences in many of the aroma intensity of vegetal aromas and flavors. Low leaf ψ and flavor attributes in 2006. Citrus, tropical fruit, wines also commonly had higher sweetness and peach and honey aromas as well as citrus, mineral lower sourness compared to wines from high leaf ψ and petrol flavors did not discriminate the wine (-1.0-1.2 MPa) zones. treatments. Some of these results can be explained by rainfall events late in the growing season including 3.2 In tegration of sensory data across vintages the harvest period. These significant rain events and vineyard sites possibly mitigated many of the leaf ψ differences that were observed during the growing season, hence There were attributes common to both vintages for influencing the final quality of the wines. Still, some wines of similar leaf ψ despite variations in the consistent trends were found; wines made from vines growing seasons. Yearly variations in some sensory of mild water deficit (-1.0 to -1.2 MPa) had more attributes between high and low leaf ψ wines within a mineral/flint and petrol aromas as well as honey vineyard may be related to differences in the mean ψ flavor, while wines produced from vines with no values of the given year. For example, vines water deficit had more vegetal aromas and flavors. designated as “low water status” had lower ψ values (< -1.3 MPa) in 2005 than those from the 2006 Summary of 2005-2006 vintages: Categorizing wines vintage (-1.0-1.2 MPa). Since the fruit was harvested based on absolute leaf ψ, as opposed to the lowest systematically through the use of data imported into and highest leaf ψ category within each site, allowed GIS software it was possible to assign an actual range interpretation of the results of within site-differences of ψ values to each wine as all the grapes used in across all vineyards and vintages. Wines produced production of the wine treatment would fall into that from vines with leaf ψ > -1.0 MPa had sensory

OENO One , 2018, 52 , 2, 145-171 - 155 - ©Université de Bordeaux (Bordeaux, France) James J. Willwerth et al.

profiles with the highest intensity ratings of vegetal with PVT, whereas R2 was associated with vegetal aromas and flavors. These wines also had the lowest and mineral/flint flavors and sour taste. Low leaf ψ honey and petrol aromas. On the contrary, wines R1 and R3 were associated with petrol and made from vines < -1.3 MPa were highest in honey, mineral/flint aromas plus Brix, FVT, and pH, whereas petrol and tropical fruit flavors and lowest in vegetal R2 was associated with higher intensity of tropical aromas and flavors. fruit flavor and sweet taste plus TA. The PCA for Henry of Pelham (Figure 5A) accounted for 83.6% of Most sensory descriptors were appropriate for this the variance in the first two PCs (60.9% PC1; 22.7% study. In terms of aroma attributes, citrus and peach PC2). High and low leaf ψ wines were well separated aromas were site specific and not very important by PC2 with high leaf ψ associated with citrus, descriptors to discriminate Riesling wines of different mineral/flint, and vegetal aromas, citrus flavor, and leaf ψ. Mineral/flint flavors and astringency were sour, plus TA, pH, and FVT. Low leaf ψ wines were also not of much importance in terms of their characterized by honey aroma, honey and tropical discriminatory characteristics of the wines. White fruit flavors, and sweetness, plus Brix and PVT. The wines are not normally described as being astringent; PCA for Paragon Vineyard (Figure 6A) accounted for however, in cooler climates Riesling wines can have 92.5% of the variance in the first two PCs (72.7% quite high acidity (i.e. some wines in this study had PC1; 19.8% PC2). High and low leaf ψ wines were TA > 13 g/L and pH < 2.9). Acids have been shown well separated by PC2 with high leaf ψ ( left of PC2) to elicit tactile sensations such as astringency associated with petrol aroma and sourness plus Brix (Thomas and Lawless, 1995); therefore, it was a and pH, and low leaf ψ wines characterized by valid descriptor but was not very important from a apple/pear flavor and sweetness plus TA, FVT and discriminatory point of view in this study. PVT. The PCA for Flat Rock site (Figure 7A) accounted for 82.8% of the variance in the first two 3.3 I Principal components analysis PCs (46.9% PC1; 35.9% PC2). Two high leaf ψ replicates (R1, R3) were located left of PC2 and PCA was used to help interpret the results of the associated with baking spice aroma, apple/pear and significant sensory attributes within each vineyard honey flavors, and sweetness, plus TA, and one in the site. Chemical variables of the must were used as upper right quadrant associated with Brix and FVT. supplementary variables to see their relationship All low leaf ψ wines were located in the lower right although most did not differ substantially between quadrant and were associated with citrus aroma and vine leaf ψ treatments (data not shown). The PCA flavor, sourness, pH, and PVT. PCA for the Myers biplots for individual vineyards are shown in Figures site explained 71.3% of the variance in the first two 3 to 9. In both vintages wines of similar leaf ψ were PCs (46.3% PC1; 24.9% PC2) (Figure 8A) Wines of clustered together with similar sensory high leaf ψ were located above PC1 and were characteristics. Sites for which both vintages were characterized by apple/pear and vegetal aromas and unavailable due to disease or winter injury are not apple/pear and sour flavors, plus Brix and PVT, depicted. whereas wines of low leaf ψ ( located below PC1) 2005: The PCA performed using significant sensory were associated with honey aroma and petrol, honey attributes of leaf ψ treatments within the Glenlake and peach flavors plus TA, pH, and FVT. PCA for Vineyard (Figure 3A) accounted for 78.2% of the Cave Spring explained 87.1% of the variance in the first two PCs (53.8% PC1; 33.9% PC2) (Figure 9A) variance in the first two PCs (51.1% PC1; 27.1% Wines of high leaf ψ were located on the positive side PC2). All high leaf ψ replicates were located on the of PC1 and were characterized by citrus and honey positive side of PC2 and associated with honey aromas, apple/pear, citrus, honey, and peach flavors, aroma, honey and citrus flavors, and astringency, as and sourness, plus Brix and pH. Wines of low leaf ψ well as with secondary variables Brix, TA, pH, FVT, (located on the negative side of PC1) were associated and PVT. The association with terpenes may relate to with tropical fruit aroma, TA, FVT and PVT. some of the more fruit-driven aromas of the wines. Low leaf ψ wines were located on the negative side 2006: The PCA performed using significant sensory of PC2 and associated with mineral, petrol, and attributes of leaf ψ treatments within the Glenlake site tropical fruit descriptors. The PCA for Chateau des (Figure 3B) accounted for 83.3% of the variance in Charmes (Figure 4A) accounted for 83.2% of the the first two PCs (55.8% PC1; 27.5% PC2). All high variance in the first two PCs (61.8% PC1; 21.3% leaf ψ replicates were located on the positive side of PC2). High leaf ψ replicates 1 and 3 (R1, R3) were PC2 (as in 2005) and were associated with petrol and located in the lower left quadrant and were associated vegetal aromas and vegetal flavor plus TA and FVT. with honey aroma and apple/pear flavor as well as Low leaf ψ wines were left of PC2 and associated

OENO One , 2018, 52 , 2, 145-171 ©Université de Bordeaux (Bordeaux, France) - 156 -

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OENO One , 2018, 52 , 2, 145-171 ©Université de Bordeaux (Bordeaux, France) - 158 -

A-2005

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B-2006

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Figure 3. P rincipal c ompon en t analysis for Rieslin g w ines, Gle nlake Viney ard, N iag ara-on -the- Lak e, ON depicting in tensity ratings o f sig nificant att rib utes on p rinc ipal co mponent s (P C1 and PC2 ), 2005 (A) a nd 2006 (B). Aroma attributes are represented by lowercase and flavor attributes are represented by uppercase.

with tropical fruit aroma and honey flavor plus Brix, (Figure 5B) accounted for 69.5% of the variation in pH and PVT. The PCA for Chateau des Charmes the first two PCs (42.6% PC1; 26.9% PC2). Leaf ψ (Figure 4B) accounted for 88.2% of the variation in replicates were not as well clustered as in 2005; high the first two PCs (64.7% PC1; 23.6% PC2). leaf ψ wines were somewhat clustered on or below Clustering of treatments was more discrete than in PC1 and associated with apple/pear and citrus 2005. High leaf ψ wines were left of PC2 (R1 slightly aromas and apple/pear, petrol, and vegetal flavors, to the right) and were associated with apple/pear plus Brix, TA, pH, and FVT, while low leaf ψ wines aroma and flavor (R1) and sour and bitter taste (R2 (R1 and R2) were above PC1 and associated with and R3), plus TA, pH, FVT, and PVT. Low leaf ψ tropical fruit aroma, honey and tropical fruit flavors, wines were located in the lower right quadrant and sweetness, sourness, and bitterness, plus PVT; low associated with peach and honey flavors as well as leaf ψ R3 was in the lower right quadrant and sweet taste and Brix. The PCA for Henry of Pelham characterized similarly to the high leaf ψ wines. The

OENO One , 2018, 52 , 2, 145-171 - 159 - ©Université de Bordeaux (Bordeaux, France) James J. Willwerth et al.

A-2005

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B-2006B2006

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Figure 4. Principal component analysis for Riesling wines, Chateau des Charmes Vineyard, St. Davids, ON depicting intensity ratin gs of signific ant attribu tes on pr inc ipal com ponent s (PC1 a nd P C2), 2005 (A) and 2 006 (B). Ar oma attrib utes are represe nt ed by lowe rcase and fla vor attrib utes are rep resen ted by upp erca se.

PCA for Paragon Vineyard (Figure 6B) accounted for first two PCs (48.9% PC1; 23.7% PC2) (Figure 8B). 85.8% of the variation with the first two PCs (47.7% As in 2005, high leaf ψ wines were located on the PC1; 38.2% PC2). High leaf ψ wines (as with 2005) positive side of PC1 and associated with citrus, were on the negative side of PC2 (R1 slightly to the honey, and peach aromas, apple/pear and citrus right) and were associated with vegetal aroma and flavors, astringency as well as TA and PVT. Low leaf flavor plus Brix, TA and PVT. Low leaf ψ wines ψ wines (R1 and R3) were below PC1 and associated were characterized by apple/pear and mineral/flint with mineral/flint and vegetal aromas and petrol, flavors, sweetness, pH, and FVT. The PCA for the tropical fruit, mineral/flint and sweet flavors plus Flat Rock Vineyard (Figure 7B) accounted for 85.9% Brix, pH and FVT (mainly R3). The PCA for the of the variation with the first two PCs (53.9% PC1; Cave Spring Vineyard accounted for 66.5% of the 32.0% PC2). High leaf ψ wines were to the negative variation in the first two PCs (40.6% PC1; 25.9% side of PC2 (better clustered than 2005) and were PC2) (Figure 9B). Wines were not as well clustered associated with baking spice aroma and pH; low leaf as in 2005; two high leaf ψ wines (R1 and R2) were ψ wines were on the positive side of PC2 and on the positive side of PC2 and R3 was on the associated with vegetal aroma and flavor, bitterness, negative side. Replicates 1 and 2 were associated Brix, TA, FVT and PVT. The PCA for the Myers with baking spice and tropical fruit aromas, peach Vineyard accounted for 72.7% of the variation in the and tropical fruit flavors, and sweetness. Two of three

OENO One , 2018, 52 , 2, 145-171 ©Université de Bordeaux (Bordeaux, France) - 160 - A-2005

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